NZ332332A - Computational method for designing chemical structures sharing common functional characteristics based on specific combinations of steric configuration and binding affinity particularly making receptors for known target molecules - Google Patents

Abstract

The application relates to computational methods for designing chemical structures sharing common useful, functional properties based on specific combinations of steric configuration and binding affinity. More particularly the present invention provides a method for producing computer-simulated receptors which functionally mimic biological receptors . The simulated receptors are designed to exhibit optimized selective affinity for known target molecules. Chemical structures are then generated and evolved to exhibit selective affinity for the simulated receptors.

Description

WO 97/36252 PCT/CA96/00166 COMPUTATIONAL METHOD FOR DESIGNING CHEMICAL STRUCTURES HAVING COMMON FUNCTIONAL CHARACTERISTICS FIELD OF THE INVENTION The present invention relates to a computer-based methods for designing chemical structures sharing common useful, functional properties based on specific combinations of stenc configuration and binding affinity More particularly the present invention provides a method for producing computer-simulated receptors which functionally mimic biological receptors The simulated receptors are designed to exhibit optimized selective affinity for known target molecules Chemical structures are then generated and evolved to exhibit selective affinity for the simulated receptors BACKGROUND OF THE INVENTION Biological receptors are linear polymers of either ammo acids or nucleotides that are folded to create three-dimensional envelopes for substrate binding The specific three-dimensional arrangements of these linear arrays, and the placement of charged sites on the envelope surface are the products of evolutionary selection on the basis of functional efficacy The selectivity of biological receptors depends upon differences m the strength of attractive and repulsive forces generated between the receptor and the substrate The magnitude of these forces varies in part with the magnitude and proximity of charged sites on the receptor and substrate surfaces Because substrates differ in the number and magnitude of the charged sites present or induced on their surfaces, as well as the spatial arrangement of these sites, binding affinity can vary with substrate structure Substrates with similar binding affinities for the same receptor have a high likelihood of sharing a common spatial arrangement of at least some of their induced and fixed charged sites If the function of the receptor is correlated with binding affinity, then substrates with similar binding affinities will also be functionally similar m 1 Printed from Mimosa their effects It is in this sense the receptor can be said to recognize or quantify similarities between the substrates Traditional methods used in molecular recognition to identify or discover novel chemical compounds or substrates for selective binding affinity to receptors are based on finding molecular common subgraphs of active substrates and using these to predict new, similar compounds A drawback to this technique is that it presupposes substrates exhibiting a similar efficacy for binding are structurally similar In many cases however structurally dissimilar substrates can exhibit similar binding affinities for the same receptor More current techniques based on quantitative structure-activity relationships (QSAR) are suited only to developing novel compounds within the same structural class and as largely inadequate at developing new molecular structures exhibiting the desired selective affinity, see for example Dean, Philip M , "Molecular Recognition The Measurement and Search For Molecular Similarity in Ligand-Receptor Interaction", in Concepts and Applications of Molecular Similarity, Ed Mark A. Johnson and Gerald M Maggiora, pp. 211-238 (1990) Recent efforts have been directed at the construction of atomic models of either pseudoreceptors, m which atoms and functional groups are connected, or mmireceptors, comprised of unconnected sets of atoms or functional groups {Snyder, J P (1993) In 3D QSAR in Drug Design Theory, Methods and Applications, Kubmyi, H Ed , Escom, Leiden P 336) Related methods involve surrounding known target ligands with a number of model atoms and calculation of the intermolecular forces generated between the ligand and the receptor model. Such models have a high correlation between calculated binding energy and biological activity (Walters, D E and Hinds, R M (1994) J Medic Chem. 37 2527) but have not been developed to the point where novel chemical structures exhibiting selective affinity for the receptor models can be produced 2 Printed from Mimosa Therefore, it would be very advantageous to provide a method for identifying non-trivial similarities between different chemical structures which are both sufficient and necessary to account for their shared properties which can rhen be used as the basis for the design of new chemical structures with useful functional properties based on specific combinations of stenc configuration and binding affinity RY OF THE INVENTION The present invention provides a method for identifying non-trivial similarities between different chemical structures which are both necessary and sufficient to account for their shared functional properties The process also provides a method of generating novel chemical structures that display similar functional properties The basic concept underlying the present invention is the use of a two-step computational process to design or discover chemical structures with useful functional properties based on specific combinations of stenc configuration and binding affinity In the first step of this process an algorithmic emulation of antibody formation is used to create a population of computer-generated simulated receptors that mimic biological receptors with optimized binding affinity for selected target substrates In the second stage of the process the simulated or virtual receptors are used to evaluate the binding affinity of existing compounds or to design novel substrates with optimal binding The method described herein provides simulated receptors which mimic selected features of biological receptors, including the evolutionary processes that optimize their binding selectivity The mimics or simulated receptors generated by the method can be used to recognize specific similarities between molecules Like antibodies and other biological receptors, the simulated receptors generated by this invention are feature extraction mechanisms they can be used to identify or recognize 3 Pmted fiom Mjmosa common or similar structural features of target substrates Binding affinity between the receptors and the target substrates is used as a metric for feature recognition Target substrates can be quantitatively categorized on the basis of binding affinity with a specific simulated receptor Compounds sharing specific structural features will also share similar binding affinities for the same virtual receptor Binding affinity between biological receptors and substrates is determined by the stenc goodness of fit between the adjacent receptor and substrate surraces, the exclusion of water between non-polar regions of the two surfaces and the strength of electrostatic forces generated between neighbouring charged sites In some cases the formation of covalent bonds between the substrate and the receptor may also contribute to binding affinity The simulated receptors generated by this process mimic the binding mechanisms of their biological counterparts Average proximity of the receptor and target surfaces and the strength of electrostatic attractions developed Between charged sites on both surfaces are used to calculate a measurement of binding affinity ^he resulting values for binding affinity are used to evaluate substrate molecular similarities Binding affinity can be globally determined, that is, dependent upon interactions between the entire substrate surface and a closed receptor or receptor envelope that completely surrounds the substrate In this case analysis of global similarities between substrates is appropriate as a basis for developing useful quantitative structure-activity relationships However, m most, if not all, biological systems, affinity is locally rather than globally determined Interactions between substrate molecules and biological receptors are generally limited to contacts between isolated fragments of the receptor and the substrate surface In this situation, analysis of global similarities between substrates is inappropriate as a method of developing structure-activity relationships, since only 4 Printed from Mimosa rragments of the substrate are directly involved in the generation of binding affinity Locally similar structures share similar structural fragments m similar relative positions and orientations uocally similar structures are not necessarily globally similar Sampling of molecular properties may be achieved by a total sampling strategy involving evaluation of global similarity, a fragment sampling strategy involving evaluation of local similarity; and multiple fragments sampling strategies involving evaluation of both local and global similarity The analysis of local similarities relies on sampling discrete regions of substrates for similar structures and charge distributions In biological receptors, localized sampling arises due to the irregularity or bumpmess of the adjacent substrate and receptor surfaces Interactions between closely opposed surfaces will predominant over interactions between more separated regions in the determination of binding affinity The proximity of the adjacent surfaces will also determine the strength of hydrophobic binding. The effective simulated receptors generated by the present method must exploit discrete local sampling of target substrates (molecules) in order to evaluate functionally relevant similarities between compounds Analysis of local similarities is complicated by two factors l) the number, location and identity of the relevant fragments sufficient and necessary for specific binding affinity cannot usually be established by simple deduction from the chemical structure of the substrate, and 2) the positions and orientations of the sampled fragments are dependent upon the underlying structure of the whole molecule The part of the present method directed to the generation of simulated receptors capable of categorizing similarities between chemical substrates is essentially a search for receptors that sample the relevant fragments of the substrates at the relevant locations in space The optimization process relies on four features of simulated receptors l) generality Printed from Mimosa wherein the receptors are able to bind with more than one substrate, 2) specificity the binding affinity of the receptors varies with substrate structure, 3) parsimony the receptors differentiate among substrates on the basis of a minimal set of local structural features, and 4) mutability alteration of the structure of a receptor can change its binding affinity for a specific substrate Encoding of the receptor phenotype m the form of a linear genotype represented by a character string facilitates the processes of mutation, recombination and inheritance of the structural characteristics of the simulated receptors Simulated receptors that satisfy these fundamental criteria can be optimized to obtain specific binding affinities for locally similar substrates using evolutionary selective breeding strategies. This is accomplished by encoding the spatial configuration and charge site distribution of the receptor in an inheritable format that can undergo alterations or mutations Like biological receptors, the simulated receptors generated by this method define a three-dimensional exclusion space Such a three-dimensional space can be outlined to an arbitrary degree of resolution by a one-dimensional path of sufficient length and tortuosity Proteins formed from linear polymers of amino acids are examples of such structures Similarly the three-dimensional structure of simulated receptors can be encoded as a linear array of turning instructions This one-dimensional encoded form of the receptor constitutes its genotype The decoded form used to assess binding affinity constitutes its phenotype During the optimization process alterations (mutations) are made to the receptor genotype The effects of these changes on the binding affinity of the phenotype are subsequently evaluated Genotypes that generate phenotypes with desirable binding affinities are retained for further alteration, until, by iteration of the mutation and selection process, a selected degree of optimization of the phenotype is achieved A variety of evolutionary strategies, including classical genetic algorithms, 6 Printed from Mimosa may be used to generate populations of simulated receptors with optimal binding characteristics Receptors generated by this method are then used to generate or identify novel chemical structures (compounds) which share the specific, useful properties of the molecular target species used as selection criteria in producing the simulated xeceptors Using interaction with the receptors as selection criteria, novel chemical structures are evolved to optimally fit the receptors Because these structures must meet the necessary and sufficient requirements for receptor selectivity, they are likely to also possess biological activity similar to that of the original molecular targets The population of simulated receptors with enhanced selectivity may also be used to screen existing chemical structures for compounds with high affinity that may share these useful properties The same process may also be used to screen for compounds with selected toxicological or immunological properties In one aspect of the invention there is provided a computer-based method of designing chemical structures having a preselected functional characteristic, comprising the steps of (a) producing a physical model of a simulated receptor phenotype encoded in a linear charater sequence, and providing a set of target molecules sharing at least one quantifiable functional characteristic, (b) for each target molecule, (1) calculating an affinity between the receptor and the target molecule in each of a plurality of orientations using an effective affinity calculation, (11) calculating a sum affinity by summing the calculated affinities, (iii) identifying a maximal affinity, (c) using the calculated sum and maximal affinities to (1) calculate a maximal affinity correlation coefficient between the maximal affinities and the quantifiable functional characteristic. 7 Printed from Mimosa (ii) calculate a sum affinity correlation coefficient between the sum affinities and the quantifiable functional characteristic, (d) using the maximal correlation coefficient and sum correlation coefficient to calculate a fitness coefficient, (e) altering the structure of the receptor and repeating steps (b) through (d) until a population of receptors having a preselected fits coefficient are obtained, (f) providing a physical model of a chemical structure encoded in a molecular linear character sequence, calculating an affinity between the chemical structure and each receptor m a plurality of orientations using said effective affinity calculation, using the calculated affinities to calculate an affinity fitness score, (g) altering the chemical structure to produce a variant of the chemical structure and repeating step (f), and (h) retaining and further altering those variants of the chemical structure whose affinity score approaches a preselected affinity score In another aspect of the invention there is provided a method of screening chemical structures for preselected functional characteristics, comprising a) producing a simulated receptor genotype by generating a receptor linear character sequence which codes for spatial occupancy and charge, b) decoding the genotype to produce a receptor phenotype, providing at least one target molecule exhibiting a selected functional characteristic, calculating an affinity between the receptor and each target molecule in a plurality of orientations using an effective affinity calculation, calculating a sum and maximal affinity between each target molecule and receptor, calculating a sum affinity correlation coefficient for sum affinity versus said functional characteristic of the target molecule and a maximal affinity correlation coefficient for maximal affinity versus said functional characteristic, and 8 Printed from Mimosa calculating a fitness coefficient dependent on said sum and maximal affinity correlation coefficients, c) mutating the receptor genotype and repeating step b) and retaining and mutating those receptors exhibiting increased fitness coefficients until a population of receptors with preselected fitness coefficients are obtained, thereafter d) calculating an affinity between a chemical structure being screened and each receptor in a plurality of orientations using said effective affinity calculation, calculating an affinity fitness score which includes calculating a sum and maximal affinity between the compound and each receptor and comparing at least one of said sum and maximal affinity to the sum and maximal affinities between said at least one target and said population of receptors whereby said comparison is indicative of the level of functional activity of said chemical structure relative to said at least one target molecule In another aspect of the invention there is provided a method of designing simulated receptors mimicking biological receptors exhibiting selective affinity for compounds with similar functional characteristics, comprising the steps of a) producing a simulated receptor genotype by generating a receptor linear character sequence which codes for spatial occupancy and charge, b) decoding the genotype to produce a receptor phenotype, providing a set of target molecules3 sharing similar functional characteristics, calculating an affinity between the receptor and each target molecule in a plurality of orientations using an effective affinity calculation, calculating a sum and maximal affinity between each target molecule and receptor, calculating a sum affinity correlation coefficient for sum affinity versus a functional characteristic for each target molecule and a maximal affinity correlation coefficient for maximal affinity versus said functional characteristic for each target molecule, and calculating a fitness coefficient dependent on said sum and 9 Printed from Mimosa PCT/CA5 6/00166 maximal affinity correlation coefficients for each target molecule, and c) mutucmg the genotype and repeating step b) and retaining and mutating those receptors exhibiting increased fitness coefficients until a population of receptors with preselected fitness coefficients are obtained In another aspect of the invention there is provided a computer-based method of designing chemical structures having a preselected functional characteristic, comprising the steps of fa) providing a physical model of a receptor and a set of target u-lecules, the mlecules sharing at least one quantifiable functional characteristic, (b) for each target molecule, (i) calculating an affinity between the receptor and the target molecule in each of a plurality of orientations using an effective affinity calculation, (n) calculating a sum affinity by summing the calculated affinities, (m) identifying a maximal affinity, (c) using the calculated sum and ;aaximal affinities to (i) calculate a maximal affinity correlation coefficient between the maximal affinities and the quantifiable functional characteristic, (ii) calculate a sum affinity correlation coefficient between the sum affinities and the quantifiable functional characteristic, (d) using the maximal correlation coefficient and sum correlation coefficient to calculate a fitness coefficient, (e) altering the structure of the receptor and repeating steps (b) through (d) until a population of receptors having a preselected fitness coefficient are obtained, (f) providing a physical model of a chemical structure, calculating an affinity between the chemical structure and each receptor in a plurality of orientations using said effective Printed from Mimosa affinity calculation, using calculated affinities to calculate an affinity fitness score, (g) altering the chemical strucutre to produce a variant of the chemical structure and repeating step (f), and (h) retaining and further altering those variants of the chemical structure whose affinity score approaches a preselected affinity score In yet another aspect of the invention there is provided a method of encoding chemical structures comprising atomic elements, the method comprising providing a linear character sequence which codes for spatial occupancy and charge for each atom of said chemical structure BRIEF DESCRIPTION OP THE DRAWINGS The method of the present invention will now be described, by example only, reference being had to the accompanying drawings in which Figure 1 is a flow chart showing relationship between genotype code creation and translation to produce a corresponding phenotype forming part of the present invention, Figure 2 is a flow chart showing an overview of the steps in the optimization of a receptor for selectively binding to a set of substrates using point mutations forming part of the present invention.

Figure 3 is a flow chart showing an overview of the steps in the process of producing a population of related receptors with optimized selective binding affinity for a set of chemical substrates and using these optimized receptors for producing a set of novel chemical substrates with common shared functional characteristics, Figure 4a shows several chemical compounds used m the example relating to examples of ligand generation, Figure 4b shows ligands 1 1 to l 4 generated by the method of the present invention in the example of ligand generation 11 Printed from Mimosa 33233 wherein each ligand has at least one orientation wherein it is structurally similar to benzaldehyde, and Figure 4c shows ligands 2 1 to 2 4 generated by the method of the present invention m the example of ligand generation relating to design of chemical structural exhibiting an efficacy for repelling mosquitoes DESCRIPTION OF THE PREFERRED EMBODIMENTS The method can be broken into two parts (A) evolution of a population of simulated receptors with selective affinity for compounds with shared functional characteristics and (B) generation of novel chemical structures having the shared functional characteristics Part (A) comprises several steps including 1) receptor genotype and phenotype generation, 2) presentation of the known chemical structure(s) to the receptor,-3) evaluation of affinity of the receptor for the chemical structure(s), 4) assessing the selectivity of the receptor for the chemical structure(s); 5) stochastically evolving a family of related receptors with optimized selective affinity for the chemical structure(s), screening chemical substrates for toxicological and pharmacological activity and using the optimized receptors to design novel chemical structure(s) with selective binding affinity for the receptors The following description of the best mode of the invention refers to various tables of molecular and atomic radius, polarizabilities, effective dipole values, and transition states and addition factors which values are found in Tables I to V located at the end of the description Flowcharts giving non-limiting examples of process calculations are attached to the end of the description m Modules 1 to 14 12 WO S7/362S2 PART Aj EVOLUTION OF POPULATION OF SIMULATED RECEPTORS EXHIBITING SELECTIVE AFFINITY FOR TARGET MOLECULES SHARING COMMON FUNCTIONAL CHARACTERISTICS (l) Ganotypq Code aad Receptor Phenotype Generation Both the simulated receptor genotypes and phenotype are computational objects The phenotypes of the simulated receptors consist of folded, unbranched polymers of spheriral subunits whose diameter is equal m length to the van der Waals radius of atomic hydrogen (®110 pm) Subunits can be connected to each other at any two of the six points corresponding to the intercepts of the spheres with each of their principal axes In the present implementation connections between subunits cannot be stretched or rotated and the centers of two connected subunits are always separated by a distance equal to the length of their sides (i e. 1 hydrogen radius) Turns occur when two subunits are not attached to the opposite faces of their common neighbour Four kinds of orthogonal turns are possible left, right, up and down Turns must be made parallel to one of the principal axes For computational sinplicity, if turns result m intersection with other subunits in the polymer, subunits are permitted to occupy the same space with other subunits A complete simulated receptor consists of one or more discrete polymers In the case of receptors consisting of multiple polymers, the individual polymers can originate at different points in space. For computational simplicity, all polymers comprising a single receptor are chosen to be of the same length in this implementation (=number of subunits) This restriction is not a requirement for functionality, and sets of polymers differing m length may be useful for modelling specific systems The structure of each polymer is encoded as a sequential set of turning instructions The instructions identify individual turns with respect to an internal reference frame based on the initial orientation of the first subunit in each polymer 13 Printed from Mirnosd Hydration of the receptor and substrate are not treated explicitly in the current implementation, instead, it is assumed that any water molecules present at the binding site are attached permanently to the receptor surface and comprise an integral part of its structure This is an arbitrary approximation and these skilled in the art will appreciate that it could be replaced by a more exact treatment (see, for example, VanOss, 1995, Molecular Immunology 32.199-211) With reference to Figure 1, the code creation module generates random strings of characters Each character represents either a turning instruction or determines the charge characteristics or reactivity of a point m the three-dimensional shape comprising the virtual receptor A minimum of five different characters are required to create a string describing the three-dimensional shape of a receptor based on Cartesian (rectangular) coordinate framework Other frameworks, e g tetrahedral structures can also be constructed using different sets of turning instructions. The characters represent turning instructions which are defined with respect to the current path of the virtual receptor structure m three-dimensional space (i e the instructions refer to the intrinsic reference frame of the virtual receptor and not an arbitrary external reference frame) Turning instructions are given with respect to the current direction and orientation of the polymer Only left, right, up and down turns are permitted. If a turn does not occur the polymer can either terminate or continue in its current direction For a rectangular system the minimum character set is Cj=no turn, C7=nght turn, C,=left turn, Q=up turn, and C=down turn It will be understood that instructions could be combined to create diagonal turns eg Ai ? = QQ , $,i =CC, etc The number of different characters that determine different charge or reactivity states is unrestricted and may be adjusted according to empirical evidence Codes may differ both in length 14 Printed from Mimosa (number of characters) and frequency with which specific characters appear m the series Kxnmple Of Genotype Creation The following example of a genotype code creation and phenotype expression will be understood by those skilled m the art to be illustrative only In this example the following conventions are employed (1) The character set used to generate the codes consists of five characters referring to turning instructions and two characters identifying a charged site "0" = no turn, "1" = right turn, "2'' = up turn, "3" = left turn, "4" = down turn, "5" = positively charged site (no turn), and n6M = negatively charged site (no turn) (2) Subunits are of two types charged or uncharged Ml charged subunits are assumed to carry a unitary positive or negative charge The uniform magnitude of charges is an arbitrary convention (3) The receptors comprise 15 discrete polymers The length of the complete code is always a multiple of fifteen The length of each polymer is equal to the total code length divided by fifteen it will be understood that receptors can be constructed from any number of discrete polymers of varying or constant length (4) The following parameters are set by the user (a) total code length (and polymer length), (b) the frequency with which each character occurs in the code string, and (c) the occurrence of character combinations Module I gives a flowchart of a sample of genotype code creation.

Bxnmnlfi of RfiCPPtor phenotype Creation Each genotype code is translated to create the three-dimensional description of its corresponding phenotype or virtual receptor From a predefined starting point a translation algorithm is used to convert the turning instructions into a series of coordinate triplets which describe the position in Printed from Mimosa 3323 space of the successive subunits comprising the receptor polymers The starting coordinates for each polymer must be given prior to translation The translation assumes that centers of successive subunits are separated by a distance equal to the covalent radius of a hydrogen atom The translation algorithm reads the code string sequentially to generate successive turns and straight path sections The interpretation of successive turns with respect to an external coordinate system depends upon the preceding sequence of turns For each polymer comprising the receptor, the initial orientation is assumed to be the same In the current implementation, the translation algorithm is described by TABLE 1 giving the input and output states If no turn occurs, the most recent values for Ax, Ay, Az and new state are used to calculate the new coordinate triplet Charge sites are treated as straight (no turn) sections The initial value of old state is 20 The following parameters can be set by the user a Starting coordinates for each polymer comprising the receptor Output is stored as a Three vectors (one for each axes {x1,x2, x3 XnMyi- ynMzi z„})- b A three-dimensional binary matrix c Separate vectors for charge site coordinates A sample process of code translation is give m Module 2 (2) Target Generation Targets are represented as molecules consisting of spherical atoms The atoms are considered to be hard spheres with fixed radii characteristic for each atomic species The hard sphere radius at which the repulsive force between the target atoms and the virtual receptor is considered to be infinite is approximated by the exposed van der Waals radius given m TABLE 2 Other estimated values of the van der Waals radius can be used in place of those in TABLE 2 16 The distance between the atomic centers of two atoms connected by a covalent bond is expressed as the sum of their covalent bond radii Covalent bond radii vary with bond order and atomic species Examples of suitable values of bond radii are given in TABLE 3 As a first approximation, bond length is assumed to be fixed (x e bond vibrations are ignored) Bond rotation is permitted, and multiple configurations of the same structure are required to sample representative rotational states Configurational stability is not considered because binding with the virtual receptor may stabilize otherv/ise energetically unstable configurations Various enery minimization algorithms can be applied to the generation of target ligands Electrical charges arising due to bond dipole moments are considered to be localized at the atomic nuclei The negative charge is carried by the atom with the larger electronegativity The dipole values used in the current implementation are given in TABLE 4 Other estimated values of dipole values can be used in place of those in TABLE 4 The affinity of the each target for the simulated receptor(s) is tested for several orientations of the target relative to the upper surface of the receptor The upper surface is defined by the translation algorithm Prior to the evaluation of binding affinity, the target and receptor must be brought into contact Contact occurs when the distance between the centers of at least one subunit of the receptor and at least one atom of the target is equal to their combined van der Waals radii in order to determine the relative positions of the target and receptor at the point of contact, the target is shifted incrementally towards the receptor surface along a path perpendicular to the surface and passing through the geometric centers of both the receptor and the target When contact occurs, the target has reached its collision position relative to the receptor The translated positions of the target atoms 17 Printed from Mimosa when the collision position is reached are used to calculate distances between the atoms of the target and the subunits of the receptor These distances are used to calculate the strength of electrostatic interactions and proximity In the current implementation, the target is assumed to travel in a straight line towards the receptor, and to retain its starting orientation at the time of contact An alternative approach would allow the target to incrementally change its orientation as it approached the receptor so that the maximal affinity position was achieved at the point of contact Although this method is functionally similar to that implemented, it is much more computationally complex In the current implementation, multiple orientations are tested at lower computational effort The current implementation allows for adjustable displacement of the path along the x and/or y axis of the receptor to accommodate larger molecules This feature is required to enhance selectivity when molecules differing m size are tested on the same receptor Prior to the calculation of the collision position, the orientation of the target is randomized by random rotation in 6° increments around each of the x, y, and z axes. Larger or smaller increments of rotation may be used Each of these random orientations of the target is unique m a given test series The reliability of the optimization process is dependent upon the number of target orientations tested as well as the number of target compounds evaluated A sample process for target presentation is given in Module 3 (4) Calculation of Affinity Approximation Strategy The current implementation is based on a simplified approximation that evaluates the principal components of affinity with relatively little computational effort The approximation is developed m the following sections However, it will be appreciated by those skilled in the art that more exact affinity calculation procedures may be utilized which give 18 Printed from Mimosa a more exact affxmty value Known computational packages for calculating more accurate affinity values may be used directly m the present process Studies of crown ethers indicate that the electron density distribution of small molecules can be used to describe the electron densities of larger compounds (Bruning, H And Feil, D (1991) J Comput Chem 12 1) Hirshfeld's stockholder method can be used to define strictly local charge distributions that are subsequently characterized by charge and dipole moment (Hirshfeld, F L (1977) Theor Chim Acta 44 129) The result is the division of the total electron density distribution of the molecule into overlapping atomic parts, the sizes of which are related to the free atomic radii It is possible to demonstrate in crown ethers that the major components of electrostatic interactions are determined by local rather than global transfers of charge between atoms Charge distribution is mainly determined by short range effects due to different chemical bonds In particular, non-neighbouring atoms contribute little to atomic dipole moments In addition, although charge transfer between atoms is also influenced by the electrostatic field of the whole molecule, calculations for crown ethers show only a very small influence on the charge distribution Calculated stockholder atomic charges and dipole moments can be used to describe electrostatic interactions (Bruning, H And Feil, D (1991) J Comput Chem 12 1) Beyond the van der Waals radius there is only a minor contribution from the atomic quadrapole moments Calculations of the electrostatic potential that take only atomic charges into account give very poor results, whereas use of the dipole moments generates improved valueB Based on these considerations, the method of the present invention incorporates an approximation of affinity between the target ligand and the simulated receptor(s) and between the 19 Printed from Mimosa simulated receptor(s) and chemical structure(s) being designed based on two measures 1 The magnitude of the electrostatic forces generated between the charged subunits of the simulated receptor(s) and the atomic dipoles of the target ligand (chemical structure) Because the charged subunits are assumed to carry nontransferable unit charges, the magnitude of these forces is directly proportional to the magnitude of the atomic dipole and inversely proportional to the distance between the simulated receptor and the atomic dipole of the ligand. 2 The proportion of the non-polar or uncharged subunits of the simulated receptor sufficiently close to the non-polar regions of the ligand for the generation of significant London dispersion forces xnniir^nnna Qafid—Efix—Affinity Calculation In Zhs Currant Iroleaeatatloat 1 The chemical substrate targets evaluated by the current implementation are assumed to be neutral (i e not ionized) molecules This is an arbitrary limitation, and an implementation applicable to charged and uncharged targets can be developed using the same methodology 2 The dipole moments are assumed to be localised at the atomic nuclei A similar analysis of affinity could be made assuming the dipole moment to be centered on the covalent bond According to Allingham et al (1989), these assumptions are functionally equivalent 3 The environment surrounding the virtual receptor is assumed to be a solvent system in which the target occurs as a solute The target is effectively partitioned between the solvent and the virtual receptor 4 At the instant for which the affinity is calculated, the target and receptor are assumed to be stationary with respect to each other, and in a specific, fixed orientation Printed from Mimosa The targets are assumed to interact with only two types of site on the receptor surface fixed charge sites (either negatively or positively charged) and non-polar sites.

On the basis of these assumptions, it is only necessary to consider the following contributions to the strength of the interaction- 1 Charge-Dipole -QW6 (4ne)zkTr< 2 Charge-Non-polar -Q2a/2(4ne) Zr1 3 Dipole-Non-polar (Debye energy) -//2a/(4ne)2r' 4 Non-polar-Non-polar (London energy) -.75 [hva2/(4ne) 2r6] In the current implementation, only relative strengths are considered by the approximation, therefore all constants are ignored. In addition the fixed charge site is assumed to be unitary and either positive or negative. On this basis, the four components can be rewritten in simplified form 1 Charge-Dipole _Ml/r4 or -fi/x2 2. Charge-Non-polar -a/r4 3. Dipole-Non-polar (Debye energy) -^a/r® or -fza 5/r3 4. Non-polar-Non-polar (London energy) -a2/r* or -a/r3 In general, terms 2 and 3 make only small contributions to long-range interactions However, both 1 and 4 contribute significantly to the interaction energy In the current implementation, most interactions between non-polar fragments are assumed to occur between adjacent alkyl and aromatic hydrogens and the non-polar subunits of the receptor Under these conditions the value of a is assumed to be approximately constant Hydrophobic Strength and Water Exclusion Contribution Solvation effects are important considerations in the generation of binding affinity For example, hydrophobic bond formation relies upon the close spatial association of non-polar, hydrophobic groups so that contact between the hydrophobic regions and water molecules is minimized Hydrophobic bond formation may contribute as much as half of the total strength of antibody-antigen bonds Hydration of the 21 Printed from Mimosa receptor and substrate surfaces is also a significant factor Water bound to polar sites of either the receptor or substrate surface can interfere with binding or increase affinity by forming cross-bridges between the surfaces The hydrophobic interaction describes the strong attraction between hydrophobic molecules in water In the case of receptor-target interactions it is taken to refer to the attraction between the non-polar fragments of the target and adjacent domains of non-poiar receptor subunits The effect arises primarily from entropic effects resulting in rearrangements of the surfaces so that water is excluded between adjacent non-polar domains Exact theoretical treatments of the hydrophobic interaction are unavailable, however, it ?s estimated that hydrophobic forces contribute as much as 50% of the total attraction between antibodies and antigens In order to estimate the hydrophobic interaction between targets and virtual receptors, the present implementation evaluates the proportion of the receptor that is effectively shielded from solvation by binding with the target All non-polar (uncharged) subunits that are within a fixed distance of non-polar atoms on the target are considered to be shielded from solvation by solvent molecules of diameter equal to or greater than the limiting distance Combined Affinity Calculation The combined affinity calculation used m the current implementation combines two measures of interaction the summed strengths of the charge-dipole interactions and a proximity measure. These affinities are assumed in the current implementation to be isotropic It will be appreciated by those skilled m the art that greater discriminatory power may be obtained if anisotropic calculations of affinity are used, although these are comput at. lonally more complex The charge-dipole interaction is calculated as D«£M±/rj.jV, where ^ the dipole moment of the ith atom of the target and rA)= the distance between the ith atom and the jth charge site on the 22 Printed from Mimosa PCT/CA96/OOI66 receptor, and the coefficient v can be set to 2, 3, or 4 The contribution of D to the total affinity is more sensitive to charge separation for larger values of v The proximity measure is calculated as P^n^/N, where n1=the number of uncharged subunits of the receptor that are separated by a maximum distance of d from the ith atom of the target with a dipole moment $0 75 Debye In the current implementation, d can range from 1 to 4 subunit diameters (this approximates the van der Waals radius of water) N is the total number of subunits comprising the receptor An affinity value A is calculated from D and P using the following relationship A«[P(D+NP/k) ]° s, where k is a fitting constant (m the current implementation, k=10000) The value of P in the equation serves two roles In the first instance it is a weighting factor As a measure of ^goodness of fit' it is use to bias the affinity value in favour of those configurations m which the non-polar regions of the target and receptor are m close contact Under these conditions, hydrophobic interactions and non-polar interaction energies will be large and will contribute significantly to the stability and strength of the bond Under these conditions the target has fewer possible trajectories to escape from the receptor ana its retention time will be prolonged In the second instance P is used to estimate the contribution of the dispersion energy to the strength of the interaction It is assumed that the dispersion energy will only be significant for uncharged, non-polar regions, and that it is only significant when the target and receptor are close to each other (i e within d of each other) The values of k and d can be adjusted to alter the relative contribution of P and D In general, P dominates for non-polar targets, whereas D is more significant for targets with large local dipoles Hydrogen bonding is apprcxiroted by paired negatively and positively charged receptor units interacting simultaneously with target hydroxyl, carboxylic or amine functional groups 23 Printed from Mimosa Alternative Approaches to Affinity caicuiatlon-Boqfl Polarizability It may be advantageous is certain cases to introduce a parameter corresponding to the relative polarizability of the target atoms into the affinity calculation In this case the equation for calculating Pz in A= IP<D+NPa/k) ]0 s is not P2=i:n1/N Instead, P2 is calculated as P2= EOiiii/N, where n1=the number of either charged or uncharged subunits of the receptor that are separated by a maximum distance of 3 from the ith atom of the target and otA is the relative polarizabiiity of the ith atom of the target For simplicity a„ could be set to 1.0 for aliphatic hydrogen The value of k must be adjusted if polarizabilities are used Sample polarizabilities based on the sums of adjacent bond polarizabilities are given in TABLE v Since polarizability is associated with displacement of the electron cloud, the polarizability of a molecule can be calculated as the sum of the characteristic polarizabilities of its covalent bonds This additivity holds for non-aromatic molecules that do not have delocalized electrons Alternative Teehnl<yu«a-P\in.. fclonal Group Specificity The affinity approximation used in the current implementation could be replaced by functionally similar computations that preserve the relationship between local charges, dispersion energy and target-receptor separation In addition, affinity measures for charged targets could be constructed The present implementation evaluates only non-covalent interactions, however, the method could be expanded by including m the virtual receptor subunits capable of specific covalent bond-forming reactions with selected target functional groups Module 5 provides a sample flowchart of the preferred effective affinity calculation used in the present invention (5) AaaesBrnant of Selective Affinity Goodness of fit between a virtual receptor and a set of target substrates is evaluated by comparing the known activity or affinity values for the targets with those obtained for the 24 Printed from Mimosa virtual receptor-target complex The maximal affinities of an optimally selective virtual receptor should be strongly correlated with known affinity measures Successive iterations of point mutations can be used to enhance this correlation between a set of substrates and a virtual receptor (Figure 2) or for optimizing selectivity of a population of virtual receptors successive iterations of the evolutionary process may be used to enhance this correlation (Figure 3).

Known values can be any index known or suspected to be dependent upon binding affinity, including (but not limited to) EDS0, IDso, binding affinity, and cohesion measures. The values tested must be positive Logarithmic transformation of the data may be required Unweighted rank data cannot be used The optimal orientation of the targets for maximal binding affinity is unknown prior to testing In order to obtain a representative measure of the range of receptor-target affinity, each target must be tested repeatedly using different random orientations relative to the receptor surface Each test uses Module 4 to evaluate affinity In general, the reliability of the maximal affinity values obtained depends upon the sample size, since it becomes increasingly likely that the sample will contain the true maximal value The same set of target orientations is used for testing each receptor Two techniques are employed m the current implementation to circumvent the need for large sample sets for the generation of optimized receptors 1) the use of a measure combining average (or sum) affinity and maximal affinity to select for receptors with higher selectivity, and 2) incremental increases m the number of orientations tested with successive iterations of the optimization process (optimization begins with a small set of target orientations, as receptors of greater fitness are generated, more orientations are tested) In the current implementation, the sum is calculated for the affinity values obtained for all the tested orientations of each target This sum affinity score is a measure of the average affinity between the receptor and the target At the same time, the maximal affinity value is also determined Correlations between the known values and both the sum affinities and the maximal affinities are calculated to give rSA2 and r,^2, respectively The origin (0,0) is included m the correlation, based on the assumption that target compounds showing no activity should have little or no affinity for the virtual receptor This assumption may not always be valid, and other intercept values may be required m some tests The correlation of using sum affinity is a measure of the average goodness of fit If this correlation is large, but the correlation between maximal affinity and known affinity is weak, the result suggests that the virtual receptor is not selective, i e multiple orientations of the target can interact effectively with the receptor Conversely, if the maximal affinity is highly correlated with known affinity values and the correlation with sum affinity is weak, the virtual receptor my be highly selective If both sum affinity and maximal affinity are highly correlated with known affinity, it is probable that the orientations sampled have identified the response characteristics of the receptor with limited error (both type I and type II errors are reduced the likelihood of either a false positive or false negative result). In some cases it may be more appropriate to minimize the correlation between the known affinities and the sum affinity, while selecting for an increased correlation between maximal affinity and known affinity Such a selection would require subtraction of the maximal affinity values from the sum total m order to remove these values as a source of confounding bias In the current implementation, a joint correlation value is used as the basis for receptor selection This value is calculated as the square root of the product of the sum affinity and maximal affinity F=( r^2 x rgA2)0 5 26 33233 This value is optimized by the evolutionary process applied to the virtual receptors Note If r,^2 and rSA2 are strongly correlated with each other, then the values contributing to rSA2 must either individually correlate closely with the maximal affinity value or contribute negligibly to the sum Alternatively the correlation (r^.^) for the (sum affinity - the maximal affinity) vs known affinity can be calculated and the measure F=(rMA2 xd-r^2))05 is maximized Use of this measure will select for receptors that have high affinity for a very limited set of target orientations Module 5 provides a flowchart of a sample goodness of fit calculation (6) The Optimization Process The objective of the optimization process is to evolve a virtual receptor that has selective affinity for a set of target substrates A highly efficient mechanism for finding solutions is required, since the total number of possible genotypes containing 3 00 instructions is 7300 or about 10253 The following four phases summarize the steps m the optimization process whereinafter each phase is discussed m more detail and example calculations given PHASE 1 Generate a set of random genotypes and screen for a minimal level of activity Use selected genotype as basis for further optimization using genetic algorithm (recombination) and unidirectional mutation techniques PHASE 2 Mutate selected genotype to generate a breeding population of distinct but related genotypes for recombinations. Chose most selective mutants from population for recombination PHASE 3 Generate new genotypes by recombination of selective mutants Select from the resulting genotypes those with the highest affinity fitness Use this subpopulation for the next recombinant or mutation generation 27 PHASE 4 Take best recombination products and apply repeated point mutations to enhance selectivity Phase Ii Evolution-generation of Primary Codo The Genetic Algorithm developed by Holland (Holland, J H (1975) Adaptation m Natural and Artificial Systems U Michigan Press Ann Arbour) can be used to search for optimal solutions to a variety of problems Normally this technique is applied using large, initially random sets of solutions In the present implementation the technique is significantly modified in order to reduce the number of tests and iterations required to find virtual receptors with high selectivity This has been accomplished by using a set of closely related genotypes as the initial population and the application of high rates of mutation at each iteration For any set of target compounds it is possible to develop distinct receptors with optimal affinity characteristics For example, receptors may bind optimally to the same targets but in different orientations The use of an initial population of closely related genotypes increases the likelihood that the optimization process is converging on a single solution Recombination of unrelated genotypes, although it may generate novel genotypes of increased fitness, is more likely to retult in divergence The objective of the first stage in the optimization process is to generate a genotype with a minimal level of affinity for the target set This genotype is subsequently used to generate a population of related genotypes A flowchart of a sample process for generation of a genotype with a minimum level of affinity is given in Kodule 6 Phase 2s Evolution-Mutation of Primary Code Mutation of the genotype comprises changing one or more characters in the code Mutations in the current implementation do not alter the number of subunits comprising the receptor polymers and do not affect the length of the genotype It will be appreciated that these conventions are arbitrary, and it will be understood that variants may have utility in some systems 28 Printed from Mimosa Mutations can alter the folding pattern of the phenotype, with resulting changes in the receptor shape space and the location or exposure of binding sites Mutations that affect the configuration of peripheral regions of the phenotype can result in shifts of the receptor center relative to the target center All mutations alter the structure of the phenotype, however, not all mutations result in changes m the functionality of the receptor Such neutral mutations may alter components of the receptor that do not affect affinity In some cases these neutral mutations can combine with subsearuent mutations to exert a synergistic affect The Breeding Population The objective of the second phase of the evolutionary process is the generation of a population of distinct but related genotypes derived from the primary genotype Members of this population are subsequently used to generate recombinants This breeding population is created by multiple muration of the primary genotype The resulting genotypes are translated and screened for selectivity The most selective products are retained for recombination Module 7 gives a flowchart for a sample process for multiple mutation of a genotype Phage 3; Evolution-Recombination The objective of recombination is the generation of novel genotypes with increased fitness Recombination facilitates the conservation of genotype fragments that are essential for phenotypic fitness, while at the same time introducing novel combinations of instructions In general, recombination coupled with selection results in rapid optimization of selectivity Module 8 provides a flowchart for a sample process for recombination of a genotype The current implementation retains the population used for recombination for testing in step 7 of Module 8 This ensures that genotypes with high selectivity are not replaced by genotypes with lower selectivity. In addition, in the current 29 Printed from Mimosa implementation, mutations (Module 7) are applied to 50% of the recombinant genotypes prior to testing (Step 7-Module 8) This step increases the variability within the recombinant population The test populations used m the current implementation range in size from 10 to 4 0 genotypes This is a relatively small population size Under some conditions, larger populations may be required Phase 4i Evolution-Maturation Progressive Mleroimitation Technique The final stage m the optimization process mimics the maturation of antibodies m the mammalian immune system A series of single point mutations are applied to the genotype, and the effect on phenotypic fitness is evaluated Unlike recombination, this process generally results in only small incremental changes to the selectivity of the phenotype The maturation process uses a Rechenberg (1+1) evolutionary strategy (Rechenberg, I (1973), Evolutionsstrategie F Frommann Stuttgart) Pt each generation the fitness of the parental genotype is compared to that of its mutation product, and the genotype with the greater selectivity is retained for the next generation. As a result, this process is strictly unidirectional, since less selective mutants do not replace their parents Module 9 shows a flowchart for non-limiting sample of maturation of a genotype During each iteration of the maturation process, only a single instruction in the code is changed If a parent and its mutation product have the same selectivity, the parent is replaced by its product in the next generation This method results in the accumulation of neutral mutations that may have synergistic effects with subsequent mutations This convention is arbitrary If recombination or maturation do not generate improved selectivity after repeated iterations, it may be necessary to repeat Phase 2 in order to increase the variability of the breeding population genome Printed from Mimosa Selected Applications The process of the present invention can be used in several areas including 1) screening for compounds with selected pharmacological or toxicological activity, and 2) development of novel chemical structures with selected functional characteristics Both applications and examples are provided hereinafter IA) Screening Method A population of receptors that have been evolved for selective affinity for a specific group of compounds sharing similar pharmacological properties can be used as probes for the identification of other compounds with similar activity, provided this activity is dependent upon binding affinity For example, a population of receptors could be evolved to display specific affinity for salicylates If the affinity of these receptors for salicylates closely correlates with the affinity of cyclooxygenase for salicylates, the receptors must at least partially mimic functionally relevant features of the binding site of the cyclooxygenase molecule. These receptorB can therefore be used to screen other compounds for possible binding affinity with cyclooxygenase This technique can also be applied to screening compounds for potential toxicological or carcinogenic activity For example, receptors could be evolved that mimic the specific binding affinity of steroid hormone receptors These receptors could then be used to evaluate the affinity of pesticides, solvents, food additives and other synthetic materials for possible binding affinity prior to in vitro or m vivo testing Simulated receptors may also be constructed to detect affinity for alternate target sites, transport proteins or non-target binding IB) Screening For Sub-Maximal Activity In some instances compounds with high affinity may have deleterious side effects or may be unsuitable for chronic administration In this case, compounds with lower binding 31 Printed from Mimosa affinity may be required Techniques such as combinatorial synthesis do not readily generate or identify such compounds In contrast, simulated receptors could be used to effectively screen for structures that display binding affinity of any specified level 1C) Measuring Molecular Similarity The selectivity of the simulated receptors can be used as a quantitative measure of molecular similarity Bmla TTf ffimulatad Receptors In the example, fictitious test values of target affinities were chosen to demonstrate the ability of the x-eceptor generation program to construct simulated receptors mimicking any arbitrarily chosen pattern of activity In this example, all receptors consists of 15 polymers Width, Length, and Depth values specify origin coordinates of the 15 polymers relative to the center of the receptor Bxanple 1 A simulated receptor was generated with the following specifications Number of subunits 24 0; Width 6, Length. 6 Depth. 25 Code "4100033103212204103333424052312013341024124022232334010032242 51014405133243400324620412100131310043112101132412022421302413 23112433 013310032305230004334140102022302140414443502652034131 0331022051414141021402134014310010231110331235210016240" Each target was tested 20 times against the receptor The affinity score for the optimized receptor was 0 9358 which is relatively low The target substrates used to optimize the receptor were benzene, phenol, benzoic acid and o-salicylic acid. The aspirin precursor o-salicylic acid is an inhibitor of prostaglandin synthesis by cyclooxygenase Benzoic acid and phenol have much lower affinity for the same site The target affinity values and the scores for the receptor are shown m Table A below which 32 Printed from Mimosa WO 97/36252 PCT/CA96/00I66 shows that the simulaLed receptor has maximal affinity for o-salicylic acid TABLE A Target Compound Target Affinity Sum Affinity Score Maximal Affinity Score Benzene 0 6 20 08 3 38 Phenol 12 8 03 4 99 Benzoic Acid 16 42 23 12 98 o-Salicylic Acid 4 4 80 33 34 71 Three test substrates were evaluated using the simulated receptor Two of the compounds are known to be less active than o-salicylic acid, m-salicylic acid and p-salicylic acid The third compound, Diflusinal is a fluorinated salicylic acid derivative of efficacy equal to or greater than that of salicylic acid The results of the evaluation are given m Table B TABLE B Target Compound Sum Affinity Score Maximal Affinity Score m-Salicylic acid 45 9 12 3 p-Salicylic acid 63 5 2 7.5 Diflusinal 117 71 2 o-Salicylic Acid 80 33 34.71 The results obtained using the simulated receptor closely match the pharmacological data for these compounds m-salicylic acid and p-salicylic acid have lower affinity scores than o-salicylic acid and diflusinal is more active than o-salicylic acid Further refinement of the simulated receptor and the use of additional, independently optimised receptors would be required to increase the certainty of these predictions of activity 33 Printed from Mimosa PCT/CA9b/U0166 PART B1DEVELOPMENT OP NOVEL COMPOUNDS WITH SELECTED FUNCTIONAL CHARACTERISTICS Evolution of Novel Liaanda A population of simulated receptors evolved for selective affinity to a set of target compounds with similar functional characteristics can be used to devise novel compounds with similar characteristics, provided these characteristics are closely correlated with the structure or binding affinity of the model compounds Using interaction with the receptors as selection criteria, novel chemical structures can be evolved to optimally fit the receptors Because these compounds must meet the necessary and sufficient requirements for receptor selectivity, these novel compounds are likely to also possess activity similar to that of the original molecular targets Overview of Process 1 Generate a population of simulated receptors with optimized selectivity for a set of characterized target compounds In some cases it may be desirable to generate several populations with different affinity characteristics For example, three populations of simulated receptors could be generated, the first mimicking the properties of the selected target site, the second mimicking a site required for transport of the ligand to its primary target and a third population of simulated receptors mimicking a target site mediating undesirable side-effects The development of a new ligand structure m this instance would require simultaneous optimization of affinity for the first two receptor populations and minimizing affinity for the third population 2 Determine the affinity of a novel primary structure for the simulated receptor population(s) 3 Modify primary structure and evaluate affinity using simulated receptor population(s) If the modification improves affinity characteristics, the modified structure is retained for further modification Otherwise a different modification is 34 Printed from Mimosa PCT/CA96/00I66 tested Previously re3ected modifications may be reintroduced in combination with other modifications 4 Step 3 is repeated until a compound with suitable affinity characteristics is obtained Note Using suitably discriminating simulated receptors it is possible to evolve chemical structures with sub-maximal affinity for a selected target site 1) Molecular Ganot-ype Code Generation Encoding of the ligand phonotype (molecular structure) in the form of a linear genotype represented by a character string facilitates the processes of mutation, recombination and inheritance of the structural characteristics of the ligand during the evolutionary process The ligands evolved by the current implementation consist of substituted carbon skeletons Each code consists of three character vectors The primary code vector contains the turning instructions for the generation of the carbon skeleton and determines the position of each carbon atom m the skeleton The secondary coae vector identifies the functional groups attached to each carbon atom The tertiary code vector specifies the position of the functional group relative to the host carbon Molecular skeletons combining atoms other than carbon (eg ethers, amides and heterocycles) can be constructed in a homologous fashion using additional characters in the code to specify atomic specT.es replacing carbon atoms in the skeleton The carbon skeleton is constructed from a series of points which form the nodes of a three-dimensional tetrahedral coordinate system During mit'al skeleton construction, the distance between nearest points is equal to the mean bond length between alkyl carbon atoms Primary code vector: ligand skeleton determinants The primary code vector consists of characters identifying turning direction relative to the current atom position Each turning direction specifies the coordinates of the next atom in the tetrahedral matrix Four directions Printed from Mimosa (1,2,3,4) can be taken from each atom, corresponding to the unfilled valences of sp3 carbon Each of the carbon atoms belongs to one of four possible states (A, B, C, D) These states correspond to the number of distinct nodes in the tetrahedral coordinate system The relationship between turn direction and the new coordinates for the next atom in the skeleton is given by the following tables The two tables B1 and B2 below embody the two turning conventions required to construct the ligands The boat convention results in the generation of a tetrahedral matrix in which closed 6-member rings (cyclohexanes) assume the boat configuration The chair convention results in the generation of a matrix m which cyclohexyl rings assume the chair configuration It is possible to combine both conventions during code generation Only the boat convention is used m the examples discussed here. 36 Printed from Mimosa "WO 97/36252 Table Bl: Boat Convention.

Current Position = (x, y, z) New Position Following Turn Current State Turn = l Turn = 2 Turn = 3 Turn = 4 A (x-,75, (x+ 75, (x, y- 864, (x, y+ 433, z- y+.433, z- z- .5) y. ) .5) z+l) B (x+ 75, y- (X- 75, y- (x, y+ 864, (X, 433, Z+ 5) 433, Z+ 5) z+ 5) Y, z- 1) C (x- 75, (x+ 75, (x, y- 864, (X, y+ 433, y+ 433, z+ .5) y, z- z+ 5) Z+ 5) 1) D (x+ 75, y- (X- 75, y- (x, y+ 864, (x, 433, z- 5) .433, Z-.5) z- 5) Y, z+l) Each turn also results in the specification of the state of the new atom New State Following Turn Current State Turn = 1 Turn = 2 Turn - 3 Turn = 4 A B B B C B A A A D C D D D A D C C C B 37 Printed from Mimosa Table B2; Chair Convention Current Position = (x, y, z) New Position Following Turn Current State Turn = 1 Turn = 2 Turn = 3 Turn = 4 A (X- 75, (x+ 75, (x, y- 864, (x. y+.433, z- y+ 433, z- z- 5) Y. -5) ) z+l) B <x+ 75, y- (x- 75, y- (x, y+ 864, (x, .433, z+.5) 433, z+ 5) z+ 5) y, z- i) C (x-,75, y- (X+.75, y- (x, y+ 864, (X, .433, z+ 5) 433, z+ 5) z+ 5) y» z- i) D {x+ 75, (x- 75, (x, y- 864, (x. y+ 433, z- y+ 433, z- z- 5) Y, -5) ) z+l) Each turn also results in the specification of the state of the new atom New State Following Turn Current State Turn = 1 Turn = 2 Turn = 3 Turn = 4 A B B B C B A A A D C D D D A D C C C B Using these relationships, primary code vectors consisting of strings of the characters 1,2,3, and 4 can be decoded to create three-dimensional arrangements of carbon atoms The resulting string of carbon atoms is allowed to fold back on itself or create closed loops, producing short side chains and ring structures Specific ring structures (for example, cyclohexanes) can be incorporated directly as specific character sequences, as shown below 38 Printed from Mimosa Secondary Code Vector; Subfltitruants A secondary code vector, of the same length as the primary code vector, is used to allocate the type of substituent attached to the carbon atom specified by the primary code vector Each substituent is identified by a single character Substituents are added singly to the carbon skeleton A single carbon atom can have more than one substituent, but only if it is specified more than once by the primary code In the current implementation, all valences not filled by substituents specified by the secondary code vector are automatically filled with hydrogen atoms during the ligand construction process Other rules could be applied for filling empty valences with atoms other than hydrogen Tertiary Vector; Substituent Bond Vector A tertiary code vector, of the same length as the primary code vector, is used to allocate the valence used for the attachment of the substituent specified by the secondary code vector The tertiary code consists of the characters 1, 2, 3, and 4 each of which refers t.c the turn directions specified for the primary code. Substituents are only allocated if the valence is not already occupied by either a carbon atom specified by the primary code vector or another previously allocated substituent Alternatively, successive substituents could replace previously allocated substituents To create carbon skeletons the primary code is constructed by creating a random sequence of characters belonging to the set {"1", "2", "3", "4"} The creation of heterocyclic structures, ethers, amides, lmides and carboxylic compounds is accomplished by substituting a carbon atom in the skeleton by a different atom specified by the secondary code The secondary code is generated from a random sequence of characters identifying substituent types The frequency of the characters can be random or fixed prior to code generation 39 Printed from Mimosa The tertiary code consists of characters belonging to the set {"1", "2","3", "4"} Ring structures can be deliberately constructed (as opposed to random generation) by adding specific character sequences to the primary code For example "431413" codes for a cyclonexyl ring A total of 24 strings code for all possible orientations of cyclohexyl rings in the tetrahedral matrix Secondary and tertiary code vectors for the ring primary codes are generated as described previously Module 10 provider* a flowchart of an example creation of code generating carbor skeletons with rings The relative positions of the entry and exit points from a ring comp -ismg part of the carbon atom skeleton are dteremined by the length of the character sequences used to generate the ring Specifically, if the sequences contains six characters, for e.>cample 431^13, then the entry and exit point will be the same1 member of the ring If the sequence is partially repeated and appended to the initial six characters, the entry point and exit point will not be the same member of the ring For example, the sequences 4314134 and 43141343141 will generate rings with exit points at the members of the rings adjacent to the entry points In the current implementation, rings are added to the skeleton by addding sequences of 6 or mroe characters to the code For the ring defined by 431413 the possible sequences used are 431413 4314134 43141343 4314134311 4314134311 43141343141 431413431413 431413431413 The conventions presented for creating a novel ligand genotype can be used to encode other chemical structures in a 40 Printed from Mimosa PCT7CA96/00166 linear format, either for storage or for introduction into the ligand evolutionary process For example, a known pharmacophore can be encoded in linear format and used as the starting point for evolving novel ligands with similar or enhanced functional properties Similarly, sets of pharmacophores interacting with a common target site can be encoded in linear format and used for recombination 3) Code Translation and Liyand Construction The code vectors are converted into three-dimensional representations of ligands in a translation process consisting of three discrete steps In the first step, the carbon atom skeleton is constructed using the primary code In the second step substituents are added to the carbon skeleton using the instructions from the secondary and tertiary code vectors Instructions from the secondary and tertiary code vectors may also specify replacement of carbon atoms in the skeleton with different atoms Instructions from the secondary and tertiary codes may also change the number and orientation of available valences present on acarbon or other atom forming part of the primary skeleton Foi example, addition of carbonyl oxygen occupies two empty valences In the third step, all valences not filled by substituentE during the second step are filled with hydrogen atoms (unless otherwise specified).

Primary decoding» Llcrand skeleton construction Primary decoding uses the turning instructions from the primary code vector to specify the positions of each carbon atom The first atom is assumed to be located at the origin of the coordinate system The first atom is assumed to occupy state A m the matrix Decoding proceeds sequentially The result of the primary decoding process is a 3 x n matrix containing the x, y, and z coordinates of each of the n carbon atoms m the skeleton Because loops and reversals are permitted, the same position in space may be occupied by more than one carbon In these cases, only one carbon atom is assumed to occupy the 41 Printed from Mimosa position As a result, the number of carbon atoms forming the completed skeleton may be less than the number of characters m the primary code vector As the primary code is read, a list is constructed from the secondary code that identifies the substituents attached to each carbon position At the same time a parallel list is constructed using the tertiary code to specify the valence occupied by each substituent.

Secondary decodlnai Subatituent additions Substituents are added sequentially to each carbon atom based on the list generated from the secondary code during primary decoding The corresponding value from the tertiary code is used to specify the valence position of the substituent relative to the host carbon If the position is already occupied by either an adjacent carbon atom, or a previously specified substituent, the substitution is not carried out Alternatively, a decoding process could be constructed in which the substitution is carried out at the next unoccupied position or the substitution replcases a previously specificed substituent The distance between the substituent and the carbon atom is calculated from look up tables of bond lengths The position data and bond lengths are used to calculate the coordinates of the substituent In the case of multi-component substituents, such as hydroxyl, nitro, and amino groups, the coordinates for each atom m the substituent are calculated relative to the host carbon After all the substituents specified by the secondary code vector are added to the skeleton, all unfilled positions remaining on the skeleton are filled with hydrogen atoms The hydrogen spJ-carbon bond length is used to calculate the coordinates of each hydrogen atom A single carbon atom can have more than one non-hydrogen substituent This can occur if the same position is specified more than once by the primary code vector The current implementation does not incorporate multiple 42 Printed from Mimosa substitutions using the secondary code directly, although this can be readily implemented Substitutions are only allowed at loci not occupied by carbon atoms forming the ligand skeleton A cumulative list is maintained of all occupied sites in the tetrahedral matrix During the secondary decoding process a list is compiled of the type, radius, and position of all the atoms comprising the ligand. This list is the basis for subsequent target generation At this stage in the process, the feasibility of the structure generated from the code sequence is not evaluated In some cases the atomic coordinates may be entered into energy minimization programs to create more realistic structures However, in the present implementataon, no assumptions are made concerning the configuration of the ligand during binding In addition, the current implementation preserves the structural uniqueness of specific configurations of the same molecule For example, the current implementation distinguishes between three rotational isomers of butane, and treats each isomer as a unique molecule The code vectors constitute the genotype of the corresponding ligand, and can be subjected to mutation and recombination with resulting changes in ligand structure The ligand structure itself is the phenotype used to evaluate binding affinity with a selected population of virtual receptors 4) Taryafc Presentation Chemical structures or target ligands are initially constructed from randomly generated codes Following decoding, the coordinates, radii, dipole moments and polarizabilities of each atom in the target ligand are obtained from look up tables of value and used to evaluate the binding affinity between the ligand and a selected population of virtual receptors 43 Printed from Mimosa The affinity of the target for each of the virtual receptors is tested for many orientations of the target relative to the receptor surfaces No assumptions are made concerning the relative orientations of the ligand and simulated receptor Prior to the evaluation of binding affinity, the target and receptor must be brought into contact The method of target presentation and calculation of affinity between the chemical structures and simulated receptors is essentially the same as discussed above m Module 4 between known target molecules and the simulated receptors ) Evaluation off Binding Affinity and Fltneaa The binding affinity of the target ligand for each of the simulated receptors used for fitness evaluation is calculated using the same effective affinity calculation method described for simulated receptor generation using the target molecules As previously noted, affinity calculations using other criteria can be incorporated into the fitness testing process but the efficacy and computational efficiency of the present invention relies in part on using the same effective affinity calculation for virtual receptor generation and generation of the chemical structures using the simulated receptor populations 6) Ligand Evolution Testing Goodnftsa of Fit Goodness of fit between a selected population of simulated receptors and a novel ligand or chemical structure is evaluated by comparing the target activity or affinity values for the ligand with those obtained for the simulated receptor-ligand complexes The maximal affinities of an optimally selective virtual receptor should be strongly correlated with the target affinity measures Successive iterations of the evolutionary process are used to enhance this correlation The target values can be set to any level of binding affinity It is not required that the ligand have the same 44 Printed from Mimosa binding affinity for all the virtual receptors used in the selection process In the current implementation, the maximal binding affinities of the optimized virtual receptors for known substrates are used to calculate target binding affinities For example, the target affinities may be set to 90% of the binding affinity of each member of the virtual receptor population for a specific substrate Alternatively, the target binding affinity may be set to zero if the interaction between the ligand and the virtual receptor is to be minimised By combining simulated receptors optimized for different sets of substrates and associating selected target affinity values with each receptor, novel ligands can be selected for specific binding affinity profiles Ligand fitness measures the match between calculated ligand binding affinities and the target affinity values The optimization process maximizes ligand fitness.

The optimal orientation of the ligands for maximal binding affinity is unknown prior to testing In order to obtain a representative measure of the range of receptor-ligand affinities, each novel ligand must be tested repeatedly using different random orientations relative to the receptor surface Each test uses Module 4 discussed in Part A to evaluate affinity In general, the reliability of the maximal affinity values obtained depends upon the sample size, since it becomes increasingly likely that the sample will contain the true maximal value Two techniques are employed in the current implementation to circumvent the need for large sample sets for the generation of optimized novel ligands or chemical structures 1 The use of a measure combining average (or sum) affinity and maximal affinity to select for ligands with optimized affinity profiles 45 Printed from Mimosa WO 97/36252 PCT/CA96/00166 2 Incremental increases in the number of orientations tested with successive iterations of the optimization process (Optimization begins with a small set of target orientations, as ligands of greater fitness are generated, more orientations are tested ) In tha current implementation, the sum is calculated for the affinity values obtained for all the tested orientations of each ligand This sum affinity score is a measure of the average affinity between the receptor and the ligand At the same time, the maximal affinity value is also determined Both sum and maximal affinities are used to test the goodness of fit between the virtual receptor and the novel ligand The fitness of each novel ligand is rated according to the difference between the calculated values of sum affinity and maximal affinity and the target values for these parameters In the current implementation, the value I calculated„max affinity - targfit max affinity | 1 + 2 x target max affinity I [calculated sum affinity - target sum affmitv| I 2 x target sum affinity ) is calculated as the fitness score for each novel ligand- simulated receptor pair FITNESS IS MAXIMAL WHEN THE FITNESS SCORE IS ZERO Target maximal affinity and target sum affinities are obtained from the regression functions developed during the evolution of optimised virtual receptors, as described m the previous sections The target values are obtained as follows target max affinity = f x maximal affinity of the most potent substrate used for virtual receptor generation target sum affinity = f x sum affinity of the most potent substrate used for virtual receptor generation 46 Printed from Mimosa 33233 where f = a scaling factor When more than one simulated receptor is used for the evaluation of ligand fitness, the fitness scores of each ligand-simulated receptor pair are summed n Ftot=E Fx l-l Icalculated max affmityi - target max affinity J + "1 2 x target max affmityi Icalculated sum affini^ - target sum affinitvi 1 2 x target sum affmityi In this case, fitness is maximized when the sum of the fitness scores is zero In some cases it may be desirable to use only the maximal affinity scores when testing a novel ligand against a panel of different simulated receptors. In this case the fitness would be given by n ^calculated max affimty1 - target max affmityil/target max affmityi a.=l In this case, fitness is also maximized when the sum of the fitness scores is zero Other methods, for example the use of a geometric mean, could also be used to measure the total fitness of a ligand tested against a series of simulated receptors Use of both the maximal affinity values and sum affinity values obtained for each simulated receptor ensures that the selectivity of the virtual receptors is implicated m the evaluation of ligand fitness In this way, the fitness of the ligand reflects not only the affinity of the ligand but also satisfaction of the stenc requirements of the virtual receptor that are the basis of selectivity 47 6a) The Optimization Process Objective To evolve a novel ligand that has selected target affinities for a set of simulated receptors A highly efficient mechanism for finding solutions is required, since the total number of possible genotypes containing 25 instructions is 256" Process (1) PHASE 1 Generate a set of random genotypes coding for ligands and screen against a set of simulated receptors to select ligands exceeding a threshold level of fitness (2) PHASE 2 The selected genotype is used as the basis for further optimization using genetic algorithm (recombination) and unidirectional mutation techniques Mitate selected genotype to generate a breeding population of distinct but related genotypes for recombination (3) Choose most selective mutants from population from population for recombination (4) PHASE 3 Generate new genotypes by recombination of selective mutants Select from the resulting genotypes those with the highest affinity fitness. Use this subpopulation for the next recombinant (repeat PHASE 3) or mutation (repeat PHASE 4) generation (5) PHASE 4 Take best recombination products and apply repeated point mutations to enhance selectivity (6) The optimization process is completed when ligands of desired fitness are generated PHASE it Evolution-generation of Primary Code The objective of the first stage in the optimization process is to generate a genotype and corresponding ligand phenotype with a minimal level of fitness This genotype is subsequently used to generate a population of related genotypes 48 Printed from Mimosa The Genetic Algorithm developed by Holland can be used to search for optimal solutions to a variety of problems Normally this technique is applied using large, initially random sets of solutions In the present implementation the technique is significantly modified in order to reduce the number of tests and iterations required to find ligands with high selective affinity This has been accomplished by using a set of closely related genotypes as the initial population and the application of high rates of mutation at each iteration For any set of target compounds it is possible to develop distinct ligands with optimal affinity characteristics For example, receptors may bind optimally to the same targets but in different orientations The use of an initial population of closely related genotypes increases the likelihood that the optimization process is converging on a single solution Recombination of unrelated genotypes, although it may generate novel genotypes of increased fitness, is more likely to result in divergence PHXSg 21 Llaand Miltation The objective of the second phase of the evolutionary process is the generation of a population of distinct but related genotypes derived from the primary genotype Members of this population are subsequently used to generate recombinants This breeding population is created by multiple mutation of the primary genotype The resulting genotypes are translated and screened for selectivity The most selective products are retained for recombination Ligands are subjected to mutation by changing characters m the genotypes (code vectors) encoding their structures These mutations change the shape of the ligand, as well as functional group placement and functional group types present on the ligand Mutations in the current implementation can alter the number of carbons comprising the ligand skeleton Hodule 11 is a flowchart of a sample process for multiple point mutation 49 Printed from Mimosa Mutations can alter the folding pattern of the ligand phenotype, with resulting changes m shape and the location or exposure of functional groups Mutations that affect the configuration of peripheral regions of the ligand phenotype can result in shifts in position relative to the receptor center Nautral Mutations All mutations alter the structure of the phenotype, however, not all mutations result in changes in the functionality of the ligand Such neutral mutations may alter components of the ligand that do not affect affinity In some cases these neutral mutations can combine with subsequent mutations to exert a synarqistic affect Sequence mutations do not change code characters directly Instead the sequence of characters m the code is rearranged Sequence mutations can alter the size of the ligand, the structural configuration and presence and location of functional groups Four types of sequence mutation are used in the current implementation a) DELETION A sequence of characters is removed from the code ABCDBA - ABEA b) INVERSION The order of characters comprising a sequence within the code is reversed ABCDEA - ABDCEA c) DUPLICATION A sequence of characters comprising part of the code is repeated ABCDEA - ABCDCDEA d) INSERTION A sequence of characters is inserted into the code.

ABCDEA - ABCDBCEA Mutations are applied m combination m the current implementation Module 12 provides a flowchart of a sample sequence mutation 50 Printed from Mimosa 3PCT/CA96/00166 PHASE 3; Generation of Recombinant Code During recombination, randomly chosen, complementary sections are exchanged between selected genotypes The objective of recombination is the generation of novel genotypes with increased fitness Recombination facilitates the conservation of genotype fragments that are essential for phenotypic fitness, while at the same time introducing novel combinations of instructions In general, recombination coupled with selection results m rapid optimization of selectivity Module 13 provides a flowchart for a sample procedure for recombination The current implementation retains the population used for recombination for testing This ensures that genotypes with high selectivity are not replaced by genotypes with lower fitness In the current implementation, multiple mutations are applied to 50% of the reconfoinant genotypes prior to testing This process increases the variability within the recombinant population The test populations used in the current implementation range in size from 10 to 40 genotypes This is a relatively small population size Under some conditions, larger populations may be required PHASB.-4-; Ligand Maturation ProareBalvft mler-nmiihw tH on fcpr.hrH rp,* The final stage in the optimization process mimics the maturation of antibodies m the mammalian immune system A series of single point mutations are applied to the genotype, and the effect on phenotypic fitness is evaluated Unlike recombination, this process generally results in only small incremental changes to the selectivity of the phenotype The maturation process uses a Rechenberg (1+1) evolutionary strategy At each generation the fitness of the parental genotype is compared to that of its mutation product, and the genotype with the greater selectivity is retained for the next generation As a result, this process is strictly unidirectional, since less selective mutants do not replace 51 Printed from Mimosa their parents During each iteration of the maturation process, only a single instruction m the code is changed in the present implementation If a parent and its mutation product have the same selectivity, the parent is replaced by its product in the next generation This methoc results m the accumulation of neutral mutations that may have synergistic effects with subsequent mutations This convention is arbitrary Module 14 provides a flowchart for a sample maturation process If recombination or maturation do not generate improved selectivity after repeated iterations, it may be necessary to repeat multiple mutataons (PHASE 2) in order to increase the variability of the breeding population genome.

EXAMPLES OP LIGAMP flEMBRATTOlf Overview The mosquito Aedes aegypti is repelled by benzaldehyde and, to a much smaller degree, by benzene and toluene (Table 1) This species is not repelled significantly by cyclohexane or hexane (Table 1) In the following test of novel ligand generation, the method is used to generate, ab initio, compounds that will be similar in repellent activity to benzaldehyde. In the first step of ligand generation, simulated receptors were constructed with high affinity for benzaldehyde and low affinity for benzene In the second step, ligands are evolved with binding affinities for the simulated receptors similar to that of benzaldehyde Mabcm 1to Responses Mosquitoes were lab-reared, 7-14 days post-emergence and unfed Experiments were conducted over six day periods at 20°C under fluorescent lighting Tests were run between 12 00 and 17 00 EDT The test populations in the four sets of trials consisted of 200, 175, 105 and 95 females Mosquitoes were provided with drinking water 52 Printed from Mimosa The tests were conducted in a 35 x 35 x 35 cm clear Plexiglas box with two screened sides forming opposite walls The screening consisted of two layers an inner layer of coarse plastic mesh and an outer layer of fine nylon mesh The box was placed m a fumehood such that air entered one of the screened sides and exited through the opposite side Air flow was <0.5cm/s.

The mosquitoes landed on the walls of the box, oriented head upwards Triangular pieces (4x4x1 mm) of Whatman #1 filter paper were used to present the stimulant compounds The tips were dipped into the test solution to a depth of 0 5 cm and used immediately Responses to the test solutions were determined as follows 1 A stationary female resting on the interior screen of the upwind wall was selected for testing 2 The treated filter paper tip was placed against the outside of the screen and positioned opposite the mesothoracic tarsus of the mosquito In all cases the initial approach was made from below the position of the mosquito 3 The tip was held in position for a maximum of 3 s and the response of the mosquito was noted.

The procedure was then repeated for a new individual Mosquitoes were tested only once each day with each compound Tips were used for five tests each (total duration of use < 30 s), then replaced Compounds were tested in random order, and each compound was tested twice on separate days Two sets of controls were conducted using untreated (dry) filter paper tips and tips moistened with distilled water positioned m the same manner as treated tips Tests of these controls were interspersed regularly among tests of the repellent compounds Responses to the controls did not vary during the course of the experiment (p>0 25) Four behavioral responses were recorded 3 No response the mosquito remained motionless 2 Take-off the mosquito flew away from its resting site. 53 Printed from Mimosa W> 97/36252 PCT/CA96/OOI66 3 Ipsilsteral leg lifting the mosquito raised the mesothoracic leg on the same side as the stimulus source 4 Contralateral leg lifting the mosquito raised the mesothoracic leg on the opposite side from the stimulus source Ipsilateral leg lifting was frequently followed by take-off, in which case both behaviours were recorded Polyethylene gloves were worn during testing an during all phases of compound preparation Table El. Moaauito responses to selected volatile confounds Compound Boiling N %Flight VLeg Lifting Relative Point ( C) Response Response Repellency* Benzaldehyde 178 130 90 178 Benzene 80 72 72 12 5 68 Toluene 110 166 67 27 94 Cyclohexane 81 eo 6 0 4 9 Hexane 69 100 4 0 2 8 Control (blank) - 450 0 ♦Relative repellency=[(%Flight Response + % Leg Response) x Boiling Point] / 100 Simulated Receptor and Liyand Generation Two simulated receptors were generated using the same selection criteria Each receptor was used independently to generate a set of ligands Molecular: Assembly l PHASE 1 RECEPTOR GENERATION A receptor was evolved with selective affinity for benzaldehyde The training targets were benzene and benzaldehyde Fifteen orientations of each target were used to calculate affinity values Results of the evolutionary process were Target Activity Level Sum Affinity Maximum Affinity Benzene 10 6 87 2 21 Benzaldehyde 5 9 75 87 13 02 The affinity score for the receptor was 0 992 Code for the Optimized 25 x 6 x 7 Benzaldehyde Receptor 54 Printed from Mimosa 231014406145 053400324221 412100131300 063112101132 412061421302 413231124335 133100333032 300043541401 022224-31514 143431012321 341310334122 101414141021 402131114311 010233120331 260214016231 PHASE 2: LIGAND GENERATION The optimized simulated receptor was used as a template for the evolution of novel ligands Four different ligands were assembled by random mutation and selection Ligands were selected for similarity with benzaldehyde The affinity values for the ligands were Benzaldehyde Ligand 1 1 Ligand 1 2 Ligand 1 3 Ligand 1 4 C,H„ClzOH CeH.iCl CBHUC1 (=0) C13H,60H(=0) Sum Affinity 75 87 74 03 G7 88 72 25 72 94 Max Affinity 13 02 12 82 15 14 12 58 11 2 Evolved ligands 1 1 to 1 4 are shown in Figure 4b At least one orientation of each ligand was structurally similar to benzaldehyde.

Molecrular Assembly 2 PHASE 1 RECEPTOR GENERATION A 25 x 6 x 7 receptor was evolved with selective affinity for benzaldehyde The training targets were benzene and benzaldehyde Fifteen orientations of each target were used to calculate affinity values Results of the evolutionary process were Target Activity Level Sum Affinity Maximum Affinity Benzene 10 25 88 8 53 Benzaldehyde 5 8 162 23 42 74 The affinity score for the receptor was 0 996 The code for the receptor was 031264441313 004422243042 223140112054 302122330134 543301114446 210043042311 323431131340 130020120133 224223503403 432003432122 002221221113 411440003113 323030313214 002321144010 000243013133 55 Printed from Mimosa PHASE 2 LIGAND GENERATION The optimized simulated receptor was used as a template for the evolution of novel ligands Four different ligands were assembled by random mutation and selection Ligands were selected for similarity with benzaldehyde The affinity values for the ligands were Benzaldehyde Ligand 2 1 Ligand 2 2 Ligand 2 3 Ligand 2 4 C„H,3C1(=0) CoH15C1(=0> C„H10CN(=O) C,Hu<=0) oum Affinity 162 23 182 4 166 5 159 7 15G 8 Max Affinity 42 74 48 97 43 0 39 0 46 5 FitnesB Score 0 135 0 02 0 05 Evolved ligands 2 1 to 2 4 are shown in Figure 4c At least one orientation of each ligand was structurally similar to benzaldehyde Compounds 2 1 and 2.4 are substituted cyclohexanone derivatives Ligand 2 2 is 5-Chloro-2, 7-nonadione and ligand 2 3 is 2-cyano-5-hexanone Ligand 1 4 contains a fragment corresponding m structure to methyl cyclohexyl ketone Experiments testing the repellency of cyclohexanone, menthone, methyl cyclohexyl ketone and 2-octanone (see Figure 4a) suggest that these ligands will also be repellent to mosquitoes (Table E2) Table B2. Mosquito responseo to selected volatile compounds Compound Boiling N V Flight V Leg Lifting Relative Point (ffC) ReBponse Response Repellency* Benzaldehyde 178 130 90 178 2-Octanone 173 BO 82 12 5 162 2-Acetylcyclo hexanone 225 100 54 24 175 Cyclohexanone 156 134 99 1 >= 154 Menthone 207 110 72 11 172 Control (blank.) - 450 0 * Relative repellency=[(% Flight Response+% Leg Response) x Boiling Point] / 100 56 Prjntpd fron Mimosa PCT/C/V96/OOV66 The method disclosed herein of designing new chemical structures exhibiting preselected functional characteristics or properties has been described by example only For example, the method may be readily practise using other known or acceptable values for polarizabilities, dipole moments, covalent radii and the like In addition, the flowcharts giving process calculation steps m the modules are meant to be illustrative only For example, the calculation of affinity may be carried out using available computational packages using fewer approximations than used herein The method of generating new chemical structures has relied upon first generating one or more simulated receptors exhibiting a preselected affinity for known target compounds with similar functional characteristics and using these receptors to generate the novel structures exhibiting these characteristics to whatever degree is desired The receptors themselves may be used for other applications besides generating novel chemical structures, for example as a means of screening for pharmaceutical or toxicological properties of known compounds Thus, it will be appreciated by those skilled in the art that numerous variations of the method disclosed herein may be made without departing from the scope of the invention 57 Printed from Mimosa TABLE Is Transition states and addition factors Old Addition factors New State for Turn = State Ax Ay Az Right Up Left Down 1 0 1 0 2 4 3 2 -1 0 0 6 1 24 3 1 0 0 1 1 22 4 0 0 -1 12 23 14 1 0 0 1 9 1 16 23 6 0 0 -1 11 2 7 0 0 -1 13 21 8 3 8 0 -1 0 7 9 24 14 9 -1 0 0 17 8 0 1 0 6 14 22 9 11 0 -1 0 22 16 6 12 12 -1 0 0 18 11 4 13 13 0 1 0 24 12 7 16 14 1 0 0 4 8 18 0 -1 0 3 17 2 18 16 1 0 0 13 17 11 17 0 0 -1 16 19 9 18 0 0 1 14 12 19 19 0 1 0 18 21 17 1 0 0 23 24 19 6 21 -1 0 0 19 22 23 7 22 0 0 1 3 11 21 23 0 -1 0 21 4 24 0 0 1 8 2 13 Formula for algorithm Input(old state, turn) -•output (Ax, Ay,Az, new state) Example Initial position (12, 34, -18), Input old state=10, turn=right Output: new state=6, Ax=0, Ay=l, Az=0, Subsequent position (12, 35,-18) 58 Printed from Mimosa WO 97/36252 PCT/CA96/00166 TABLE 2t Van der Waals Radii Element HFONCClSBrPI Van der Waals 110 140 150 150 170 1B0 180 190 190 200 Radius (pm) Relative Radius 0 5 0 64 0 6B 0 68 0 77 0 82 0 82 0 36 0 86 031 (H=0 5) Based on N S Issacs, 1987 Physical Organic Chemistry Longman Scientific and Technical, New York 828 pp 59 Printed from Mimosa TABLE 31 Covalent Bond Radii (cm) Bond Order First H 28 B C N 0 F First 88 77 70 66 64 Second 66 5 60 55 Third 60 2 55 Aromatic 70 Si P S CI First 117 110 104 99 Br First 114 Based on values in N S Issacs (1987) . 60 Printed from Mimosa TftttT.lt 4. flmnplw dinnln valiiWR iinad fnr ch^Taft b^a assignments.

Bond Atom Dipole Value (Debye) C-H H +0 35 or +0 084* C no charge assigned ArC-H H + 0 6 C -0 366 or no charge assigned =C-H H + 0 336 C -0 6 or no charge assigned* c=o 0 -2 7 C no charge assigned* or +1 35 c-o-c O -0 9 C-OH H +1 5 or +1 7 O -1 1 c-nh2 H +1 3 N -1 3 c-ko2 0 -2 0 N +4 0 C"N N -3 7 C no charge assigned c-s-c s in thiophene or dimethyl sulphide +1 5* (may be negatively charged in contexts) C-HsC N in pyridine or CH,-N=CH. +i 5 or +1 3 Ar-F or CcC-F F -1 3 C-F F -1 8 Ar-Cl or C-C-Cl C -1 7 C-Cl CI -2 1 Ar-Br or C=C-Br Br -1 7 C-Br Br -2 0 C-I I -2 0 •Preferred uner most conditions Bach target atcan 10 described fully by a set of eight values {x,, y1( zit rt, , cj; , <J , p } where ix , iy andi z are the positional coordinates relative to the geometric center of the molecule, ri=the van der Waals radius, br^the bond or covalent radius, cr^tfre collision radius (=ri+0 5), Oi«the polarizability, and dt=the effective dipole moment value 61 Printed from Mimosa TABLE 51 Relat-.lv» Eff^i-sYft pm*rjyjgbiiitiwB Pqt- Selectad Target Atnma Atom Context Relative Polarizability (a. > H C-H l o H N-H 1 1 H 0-H 1 1 H S-H 3 0* F C-F 1 5* CI C-Cl 4 0 Br C-Br 5 8 I C-I 8 9* C C-CHj 3 7 C C-CH-.-C 3 5 C C-CC:-H 3 2 C CaCH 4 5 C C=CH-C 4 3 C C.CC, 4 0 C C-C-H 4 9* C C^C-C 4 6* C Arene ring 4 3* or 2 6 (based on benzene (delocalized electron cloud)) C C-C=N 4 0 C Cj-C-O- 3 6 C CjH-C-O- 3 8 C CH-C-O- 4 1 C Hj-C-0- 4 4 C C,-CbO 3 6 C CH-C=0 3 8 C Ct-C-N ? C CH-C=N ? C Cj-C-N 3 1 C CjH-C-N 3 3 C CHj-C-N 3 6 C H,-C-H 3 8 0 C-O-H 2 1 0 C«0 2 1 0 C-O-C 1 8 O NO? 1 9* N C-NHj 3 1 N C-NH-C 2 8* N C-NC, 2 5* N C-NO^ 4 6* (may be larger in Bmall molecules) N CaN 3 2 S C*S 7.7 S C-S-C ? S C-S-H 5 0 * By calculation from molecular polarizabilities.

? Values can be determined from appropriate molecul&r data.

Printed from Mimosa 233 MODULE 1: CODE GENERATION FOR SIMULATED RECEPTORS Step 1 Input code generation parameters i) code length; and li) instruction frequency Step 2 Initialize empty character string to store code Step 3 Generate random number.

Step 4 Based on random number and instruction frequency, select a character {'0', vl', , "6'} to concatenate to code string Repeat Step 4 until string length equals preset code length Step 5 Output code MODULE 2: CODE TRANSLATION FOR SIMULATED RECEPTORS Step 1 Input origin coordinates for polymers comprising receptor Step 2 Input code for polymer Step 3 Read first character from code Step 4 If character is a turning instruction, use translation algorithm to determine subunit coordinates otherwise step 7 Step 5 Store subuniL coordinates Assign a charge value of u to subunit Step 6 If character is not the last character m code, repeat step 3 otherwise step 10 Step 7 If character is a charge instruction, use translation algorithm to determine subunit coordinates assuming no turn Step 8 Store subunit coordinates Assign charge value of +1 or -1 to subunit based on character Step 9 If character is not the last character in code, repeat step 3 otherwise step 10 Step 10 Repeat steps 2 to 9 for each of the polymers comprising the receptor Step 11 Output coordinates and charge values of subunits 63 3323 MODULE 3: TARGET PRESENTATION Step 1 Input coordinates and radii of target atoms (xtx, yti7 zt^radiusj (i=number of atoms m target) Input coordinates of receptor (xr.j,yrj,zr3,charge.,) (j=number of subunits m receptor) Step 2 Generate random angular (A0,Acp)and translation values (kx,ky) Step 3 Rotate and translate atomic coordinates by random amounts Step 3a Convert target coordinates to polar form (xti#ytif zti7 radiusj =* (6^ , p1, radiusj Step 3b Add random changes to angles (Qit (J)^, pt, radiusj =» (Gi+AG, cJ>±+A<(>, Pi, radius1) Step 3c Convert to rectangular coordinates (Q^AG, ((Ji+Atjs, px, radiusj => (x.,, yL, zlf radiusj Step 3d Add random translation (xn1,yn1, zn1( radiusj = (xi+kx,yi+ ky, z±, radius^ Step 4 Center target coordinates on origin (0,0,0) Step 4a Find maximum and minimum values of xn±, yn^ and znx.

Step 4b Find geometric center of receptor xncenter = - xnniinimum)/2; yncenter = (yi^ axiiTum ~ y^minimum) /2/ ^^center ( ^^•maximura — ^^minimum ) / 2 Step 4c Calculate centered coordinates (xnc.,, yncj, znc-j) = (xn3 - xncenCer, Ynj " Yncenter' ~ ^center) Step 5 Use atomic radii and transformed coordinates (xnc^ ynCi, znc^ radiusj,) to construct collision surface of target g(xg,yg) =zg Step 5a Create a grid with spacing equal to the diameter of the receptor subunits (=1) Coordinates of grid 64 { Xn£ (XTln,]nJ.Riun°**^^ccnt»r) / Int {xriJT,irjj(rUjr'" )+l 01 Inc (xn^jn^piup-xn^rttfi.) 11 Infc {xnni-ixiinup -xnrrn^r) } jr(,e{lnt (yn^ni^-yiWcr), Int (yn, tninuw-" yn entei) Q r Int {ynnidXimun,"yn enter ^ "1' Int (yiw iTium~y^center) } Set the initial values of g(xv,y,) to 0 at all points on the grid Step 5b For each atom (i) set the g(xa/yg) (height) value of each grid point (x,,yq) according to the following rule For i=l to number of atoms in target If (xncI-xp)2+(ync1+yp)J<radiust' then g(x,,yg) = minimum (g<x,,y5) , zncj - radius4) Else If (xnci-xp) 2+(ynct+yp) (radiuSi + 5)^ then g (Xq,yg) = minimum (g(x,,y„) , znc^ (radius»/2)} Else gfx^y,) = minimum lg(Xg,yg), 0) Next i Step 6 Center receptor coordinates on origin (0,0) Step 6a Find maximum and minimum values of xr^yrj and zr5 Step 6b Find geometric center of receptor ^^Venr^r — (^^■axiroum ~ /2, yrcen ►■er = (y^narinum ~ yrm irumun )/2, zrce.nc»r = (2rmaximum " /2 Step 6c Calculate centered receptor coordinates {xc,,ycj( ZC,) — (xrj "X^n gr, y^*3 ~ Ycenten Step 7 Construct collision surface of receptor s{x3/ys)=z5 using the centered receptor coordinates according to the following rule Set all initial values of sfxe-j, yc.,) to 0 for 3=1 to the number of subunits m receptor if zc< > s(xcj, ycj) then s(xc,, yc^szc, 65 Printed from Mimosa y^cent«r ) } next j Step 8 Find minimal separation between collision surface of receptor and collision surface of the target Calculate difference matrix d(xn,yr) as follows for all Xq£ {Int (xnmlnimun,-xncenttfr) , Xnt (xnn^nirauni~ ^^-certlcr) 0/ Int (xnmaX|mun"*®^center ) ' Int { Xn^xlmurr " Xllc^nrcr) } and yq <r{lnt , Xnt. (yTVinimum — 01 Int (ynmaximum""yncwnt«r) ■ 1 f Int (yrVajrjmuTO" calculate dCx^y,) =(h(xq/yg) -zr^ inJmum+Zrijnaj^nuji, ) + (s(xg,yg) +zr„( nlmum 2rBaxABIUI1) For all Xg,yg find the minimal value of d(Xg,y,,) = ^rain d,,ir is the minimal separation di0,_~mce Step 9 Transform target and receptor cooi 7 nates for collision configuration For the receptor (xreceptor.,, yreceptor,, zreceptor^) = (xc3, yCj, ZCj + Zrmi„im„ — ) For the target (xtarget^ytargeti., ztargetx) = (xncL, ynci( znci + ^^iiaxlmuiq-dnln ) Step 10 Use (xtarget,, ytargett, ztargetj and (xreceptor.,, yreceptor^, zreceptorj) for affinity calculations Repeat Steps 2-9 for each target configuration tested MODULE 41 AFFINITY CALCULATION Step 1 Input collision coordinates of target and receptor (xtarget^ytarget^ztargeti) and (xrecoptori;yreceptorv zreceptor,) where i=number of atoms in target, j=number of subunits in receptor 66 Printed from Mimosa Step 2 Input dipole moment values for target dip(i^ Input charge values for receptor charge(3) Step 3 Input threshold value for proximity calculation THRESHOLD Step 4 Calculate dipole affinity value Step 4a For each charged subunit (charge{3) *0) calculate e(i,3) = dip(1)/((xtargeti - xreceptorj) 2+(ytargetx - yreceptorj) 2+(ztargeti - zreceptor., )2)15 Step 4b Calculate the sum of e(i,j) for all combinations of 1 and j with charge(3) *0 DIPOLE=S e(i,:) Step 5 Calculate proximity value (this step could be replaced by a calculation based on polarizability) Step 5a For each target atom with |dip(i)| s 0 75 Calculate 1 (i, j ) = ((xtargeti-xreceptor^) 2+ (ytargeti-yreceptor,) 2+ (ztarget^zreceptorc3 )2)0 5 If l(i,]) < THRESHOLD then prox(i,;j) = 1 Step 5b Calculate the sum of prox(i,;]) for all combinations of 1 and 3 with |dip(i)| < 0 75 PROXIMITY=E prox(1,J) Step 6 Calculate affinity value for target substrate combination = AFFINITY AFFINITY= (PROXIMITY/J)((PROXIMITY/10000)+DIPOLE) MODULE 5: GOODNESS OF FIT CALCULATION Step 1 Input known target efficacy or affinity values (yk), k=number of targets tested Step 2 Input collision coordinates of targets and receptor (xtargeti, ytargeti, ztarget4) and (xreceptorj, yreceptor.j, zreceptorc.,) ik = number of atoms m target k j = number of subunits in receptor Step 3 Input number of target orientations to be tested (=m) 67 Step 4 Use Module 5 to obtain affinity values for each target and target orientation (= AFFINITY,.,*) Step 5 Determine maximum affinity (MA.J and sum affinity (SAJ values for each target Step 6 Calculate correlation coefficients for maximum affinity (MAk) vs known target efficacy or affinity values (yj and for sum affinity (SAk) vs known target efficacy or affinity values (yv) Step 7 Calculate fitness coefficient F F13 ( I'Mff x rsA~) Alternate Step 6' Calculate correlation coefficients for maximum affinity (MA*) vs known target efficacy or affinity values (y„) and rSA.MA'' for sum affinity (SAx) - maximal affinity vs known target efficacy or affinity values (yk) Step 7' Calculate fitness coefficient F ( Zma" X (1- rsA-KA ) ) MODULE 6: GENERATE GENOTYPE WITH MINIJOMJ LEVEL OF AFFINITY Step 1 Set minimal fitness threshold Step 2 Generate random genotype (Module 1) Step 3 Translate genotype to construct phenotype (Module 2) Step 4 Test affinity of phenotype for targets (Modules 3, 4, 5, 6) Step 5 If the fitness of the phenotype exceeds the fitness threshold then discontinue code generation and pass code to phase 2 Otherwise repeat steps 1-5 68 Printed from Mimosa MODULE 7: Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 codes. Step 8 MULTIPLE MUTATION Input primary code (from phase 1) Set number (=q)of mutations per code (Current implementation mutates 2 5-5% of characters in genotype) Input population size ( = p) Select a position in the genotype at random Replace the code character at that position with a different character chosen at random Repeat steps 4 and 5 until q times Repeat steps 4-6 to generate a total of p new Apply Modules 1-6 to test fitness of mutant population Select subpopulation with highest selectivity for use m Phase 3 MODULE 8: RECOK33INATI ON Step 1 Set population size (= P) Step 2 Select two codes at random from population generated by Phase 2 Step 3 Select a position in the genotype at random Step 4 Generate a random number for the number of characters to exchange Step 5 Swap characters between codes beginning at selected position Step 6 Repeat steps 2-5 until P new genotypes have been generated Step 7 Apply Modules 2-6 to test fitness of mutant. population Select subpopulation with highest seJectivity for next recombination series or for Phase 4 maturation 69 Printed from Mimosa MODULE 9: MATURATION Step 1 Input parental code derived from Phase 3 Step 2 Set number of iterations Step 3 Select a position in the parental genotype at random Step 4 Replace the code character at that position with a different character chosen at random Step 5 Test selectivity of parental code (FP) and mutation product (FM) using Modules 2-6 Step 6 If Fm ? Fp replace parental genotype with mutation product Step 7 Repeat steps 3-6 for required number of iterations MODULE 10: CREATION OF CODE GENERATING CARBOH SKELETONS WITH RINGS (6 Member Rings, Entry point = Exit Point) Step 1 Set length of code Set vl, v2, v3, vn (frequencies of substituent groups) Set prob_ring (frequency of ring code sequence) (0 & prob_ring s l) Step 2 initialize prime_code = "" Initialize second_code = "" Initialize third_code = "" Step 3 Create character strings Repeat step 4 until code length is obtained 70 Printed from Mimosa Step 4a Else Step 4b Step 4c If prob_nng > random (0 < random s 1) Then Assignment of characters for ring (boat convention) Set new_character_l to randomly selected member of {'431413', '314134', '141343', '132132', '321321', '213213', '123123', '231231', '312312', '421412', '214124', '141242', '324234', '242343', '423432'} Assignment of characters for substituents Set new_character_2 to six randomly selected members of {cl, c2, c3, , cn } using frequ .ncies vl, v2, v3, vn (cl cn are characters specifying different functional groups) Assignment of characters for substituent valences Set new_character_3 to six randomly selected members of {'1', '2', '3','4'} Assignment of single (non-ring) characters for primary code Set new_character_l to a randomly selected member of {'1', '2', '3', '4' } Assignment of characters for substituents Set new_character_2 to a randomly selected member of {cl, c2, , cn} using frequencies vl, v2, vn Assignment of characters for substituent valences Set new_character_3 to a randomly selected member of {'1', '2', '3','4' } Concatenate new characters to code strings Prime_code = Prime_code & new_character_l Second_code = Second_code & new_character_2 Third_code = Third_code & new_character_3 71 Printed from Mimosa WO 97/36252 PCT/CA96/00166 MODULE 11: MULTIPLE POINT MUTATION Step L Input primary code Step 2 Set number (= q) of mutations per code (Current implementation mutates 2 5-5% of characters in genotype) Step 3 Input population size (= p) Step 4 Select a position in the genotype at random Step 5 Replace the code characters at that position m each of the code vectors with different characters chosen at random Step 6 Repeat steps 4 and 5 until q times Step 7 Repeat steps 4-6 to generate a total of p new codes.

Step 8 Test the fitness of each member of the mutant population Select subpopulation with highest fitness for use m recombination or additional multiple mutation MODULE 12: SEQUENCE MUTATIONS Step 1 Set PDEl/ Pinv/ Pin*, and P^t as threshold levels for the occurrence of mutations ( 0 s Px £ l) Step 2 Generate a random position (= x) in the code (0 s p s. Length of code) Step 3 Generate random length of sequence ( = L) (0 £ L * Length of code - x) Step 4 Copy sequence from code starting at x and extending for a total of L characters Step 5 If 0 £ P1NV * Random Number s l Then Reverse the order of the characters in the string Step 6 If 0 s Pnuri £ Random Number s 1 Then Copy the sequence and concatenate copy to sequence Step 7 If 0 s Pnrj. s Random Number s 1 Then Eliminate L characters from the code starting at position x 72 Printed from Mimosa WO 97/36252 PCT/CA9W00166 Else Replace sequence m code with sequence generated in steps 5 and 6 Step 8 If 0 5 Plss s Random Number 5 1 Then Generate a position (= y) at random m code (0 s y s Length of code) Insert sequence generated by steps 5 and 6 at position y MODULE 13: RECOMBINATION Step 1 Set population size (= P) Step 2 Select two codes at random from population generated by multiple mutation Step 3 Select a position m the genotype at random.

Step 4 Generate a random number for the number of characters to exchange Step 5 Swap characters between each of the three code vectors beginning at selected position Step 6 Repeat steps 2-5 until P new genotypes have been generated Step 7 Test the fitness of each ligand in the resulting mutant population Select subpopulation with highest fitness for next recombination series or for maturation 73 Printed from Mimosa PCT/CA.96/00166 MODULE 14: MATURATION Step l Input parental code derived from recombination Step 2 Set number of iterations Step 3 Select a position m the parental genotype at random Step 4 Replace the code characters at those positions in each of the code vectors with a different characters chosen at random Step 5 Test fitness of parental code (Ff) and mutation product (Fm) using Modules 4 and 5 Step 6 If Fh > FP replace parental genotype with mutation Droduct Step 7 Repeat steps 3-6 for required number of iterations 74 Printed from Mimosa

Claims (1)

1. AMENOED Sr.t- WHAT IS CLAIMED IS 1 A methoc r-r^ing on a computer for designing cnemical structures ia/_ng a preselected functional characteristic comprising the steps of \a/ procuc_rg a pnysical model of a s_mul=tec receptor pnenotype e*-coaed in a linear cnaracter sec^erce, ana providing a set cf target molecules snaring at least cne quantifiable functional characteristic,;(b) for eacn target molecule,;(I) calculating an affinity between the receptor anc tne target molecule m each of a plural_ty of orientations using an effective affin.ty calculation,;in) calculat.ng a sum affinity ov surname tne calculated affinities,;(111) identifying a maximal affinity,;>c) usmc tne calculated sum and max_mal affinities to;(i) calculate a maximal affinity correlat.on coefficient oetween tne maximal af f-^Jtit ies and the quant if iar>ie functional cnaractenstic,;(II) calculate a sum affinity correlation coefficient oetween tne sum affinities and tne quantifiable functional characteristic,;(d) using tne maximal correlation coefficient and sum correlation coefficient to calculate a fitness coefficient,;(e) altering the structure of tne receptor ana repeating steps (b) through (d) until a population cf receptors having a preselected fitness coefficient are obtained,;(f) providing a physical model of a chemical structure encoded in a molecular linear character sequence, calculating an affinity between the chemical structure and each receptor in a plurality of orientations using said;75;effective affimtv calculation, using the calculated affinities to calculate an affinity fitness score,;(gj alten~g the chemical structure to produce a variant of tne cnemical structure and repeating step (fv ,;and;■n; retaining and further alteri-g these /anarts of the chemical structure wnose affinity score apprcacres a preselected affinity score;2 The method according to claim 1 w.ierem tne linear cnaracter sequence encoding for said receptor pnerctype is produced by generating a receptor linear cnaracter sequence whicn codes for spatial occupancy and charge, and wr.erein the step of producing a physical model of a cnemical struct-re comprises generating said molecular linear cnaracter sequence vmcn codes for spatial occupancy aria charge;3 The metnea according to claim 2 wherein saia effective affinity calculation comtnStses two measures, tne first oeing a proximity measure wherein the proportion of uncnargsd portions on said simulated receptors being sufficiently close to non-polar regions on said molecular structure to generate effective London dispersion forces is estimated, and the second being the summea strengths of cnarge-aipoie electrostatic force interactions generated between charged portions of said simulated receptor and dipoles present m said molecular structure;4 The-method according to claim 2 wheiem said step of calculating the affinity fitness score includes calculating a sum and maximal affinity between the molecular structure and each receptor, the fitness score being calculated as;76;£ {Icalculated maximal affinity - target maximal affinityl / target maximal affinity}and wherein said preselected fitness score is substantially zero;5 The method according to claim 2 wherein said step of calculating the affinity fitness score includes calculating a sum and maximal affinity between the molecular structure and each receptor, the fitness score being calculated as;£ { ( Icalculated maximal affmity-target maximal affinityl / 2 x target maximal affinity) + ( Icalculated sum affmity-target sum affimtyl/2 x target sum affinity)}, and wherein said preselected fitness score is substantially zero;6 The method according to claim 2 wherein said sum affinity correlation coefficient is rSA2, said maximal affinity correlation coefficient is r,^2, and wherein said fitness coefficient is FMrw2 x rSA2)0 5, and wherein said preselected fitness coefficient is substantially unity;7 The method according to claim 2 wherein said sum affinity correlation coefficient is rSA-MA2, said maximal affinity correlation coefficient is r^2, and wherein said fitness coefficient is F- (r^2 x (1- rSA~MJv2) ) ° S' and wherein said preselected fitness coefficient is substantially unity;8 The method according to claim 2 wherein said molecular linear character sequences comprise a plurality of sequential character triplets, a first character of said triplet being randomly selected from a first character set specifying position and identity of an occupying atom m a molecular skeleton of said molecular structure, a second character of said triplet being randomly selected from a second character set specifying the identity of a substituent group attached to said occupying atom, and a;77;third character of saia triplet being randomly selected from a third character set specifying the location of said substituent or the atom specified by said f_rst cnaracter of tne triplet;S The ~ietrod according to claim 3 whereir the molecular linear cnaracter sequence is decoced _smg an effective molecular assembly algorithm w-.ich secuerc-ally translates eac. triplet from said molecular l_near sequence and thereafter fills unfilled positions on saia molecular skeleton with nydrogen atoms;10 The method accoraing to claim 3 wnerein the step of altering said molecular structure includes at least one of the following steps i) mutating saia molecular genotype dv randomly mcercnangmg at least one cf said first, second and third cnaracters of at least one triplet from tne associated character sets, n) deletion wnerein a triplet from molecular gerotvpe is deleted, m) duplication wherein a triplet m tne molecular genotypS^s duplicated, iv) inversion wherein the sequential urder cf one or more triplets m tne molecular genotype is reversed, ana v) insertion wherein a triplet from the molecular genotype is inserted at a different position in the molecular genotype;11 The method according to claim 10 wnerein the step of mutating said molecular genotypes includes reccmoming randomly selected pairs of said retained mutated molecular genotypes whereby corresponding characters m said molecular linear sequences are interchanged;12 The method according to claim 2 wherein each character m tne receptor linear character sequence specifies one of either a spatial turning instruction and a charged site with no turn;78;TENDED S'HfcS;13 The me trod accoramg to claim 12 wherein said receptor phenotype comprises an least one linear polymer provided with a plurality of subunits, one of said subunits Joeing a first suburit m sa_d at least one linear polymer;14 The metnca according to claim 13 vnerem said receptor linear cnaracter sequence is cecodea using an effective receptor assembly algorithm m which turning instructions applied to eacr. sucun.it subsequent to said first subunit are made relative to an initial position of said first suounit;15 The methoc according to claim 14 wnerein said cnaracters specifying spat.al turning instructions code for no turn, right turn, left tarn, up turn, down turn, and wherein cnaracters specifying cnarge sites code for positively cr.arged site with no turn, and negatively cnarged site with no turn;16 The method according to {j3iim 14 wnerein said subunits are substantially spherical having a Van der Waals raaii substantially equal to tne Van der Waals radius of nyarogen;17 The method according to claim 15 wherein the step of altering said receptor genotype includes at least one of the following steps i) deletion wherein a character from the receptor genotype is deletea, 11) duplication wnerein a character in the receptor genotype is duplicated, in) inversion wherein the sequential order of one or more characters m the receptor genotype is reversed, and iv) insertion wherein a character from the receptor genotype is inserted at a different position in the genotype;79;/V!EW->;18 The Tistnoa according to claim 17 wherein tne steo of mutating saia receptor genotypes includes recomDin.ng ranaomly selected pa.rs of said retained mutated receptor genotypes wrsreoy corresponding characters m said receptor linear sequences are mtercnanged;19 A. metnoc running on a computer for screening chemical structures for preselected functional characteristics, comprising a) procuc_ng a simulated receptor genotype by generating a receptor linear character sequence whicn codes for spatial occupancy and charge,;o) decoding the genotype to produce a receptor phenotype, prov_d_r.g at least one target molecule exhibiting a selected functional cnaracteristic, calculating an affinity between the receptor and each target molecule m a plurality of orientat-or.s using an effective affinity calculation, calculating a sum and maximal affinity between each target molecule and receptor, calculating a sum affinity correlation coefficient fcSiJ'sum affinity versus said functional characteristic of the target molecule and a maximal affinity correlation coefficient for maximal affinity versus said functional characteristic, and calculating a fitness coefficient dependent on said sum and maximal affinity correlation coefficients,;c) mutating the receptor genotype and repeating step b) and retaining and mutating those receptors exhibiting increased fitness coefficients until a population cf receptors with preselected fitness coefficients are obtained, thereafter d) calculating an affinity between a chemical structure being screened and each receptor m a plurality of orientations using said effective affinity calculation, calculating an affinity fitness score which includes calculating a sum and maximal affinity between the compound;80;and each receptor ana comparing ac least one of said sum ana maximal affinity to the sum and maximal affinities between said at least one target and saia population of receptors //hereby saia compar_scn is indicative of the level of functional activity of said chemical structure relative to said at least ere target molecule;20 The method according to claim 19 wnerein saia effective aff_nity calculation comprises two measures, tne first oemg a proximity measure wnerein a proportion of uncharged portions on said simulated receptors oemg sufficiency close to non-polar regions cn said molecular structure to generate effective London dispersion forces is estimated, and the second being tne summed strengths cf chorge-dipole electrostatic force interactions generated between charged portions of said simulated receptor and dipoles present m said molecular structure;21 The metnod according to claim 20 wnerein tne fitness score is calculated as Y( 'circulated maximal affinity - target maximal affinityl/target maximal affinity};22 The metnod according to claim 20 wnerein tne fitness score is calculated as;£ { (Icalculated maximal affmity-target maximal affinityl / 2 x target maximal affinity);+ (Icalculated sum affmity-target sum affinity' / 2 x target sum affinity)};23 The method according to claim 20 wherein said sum affinity correlation coefficient is ra2, said maximal affinity correlation coefficient is r^2, and wherein said fitness coefficient is F=(r^2 x r^2)05, and wherein said preselected fitness coefficient is substantially unity;81;24 The method according to claim 20 wherein said sum affinity correlation coefficient is said maximal affinity correlation coefficient is r^2, and wherein said fitness coefficient is F=(r^3x (l-r^-^2) )0 s,;and wherein said preselected fitness coefficient is substantially unity;25 The method according to claim 20 wherein each character m the receptor linear character sequence specifies one of either a spatial turning instruction and a charged site with no turn;26 The method according to claim 25 wherein said receptor phenotype comprises at least one linear polymer provided with a plurality of subunits, one of said subunits being a first subunit m said at least one linear polymer;27 The method according to claim 26 wherein said receptor linear character sequence is decoded using an effective receptor assembly algorithm m which turning instructions applied to each subunit subsequent to said first subunit are made relative to an initial position of said first subunit;28 The method according to claim 27 wherein said characters specifying spatial turning instructions code for no turn, right turn, left turn, up turn, down turn, and wherein characters specifying charge sites code for positively charged site with no turn, and negatively charged site with no turn;29 The method according to claim 28 wherein said subunits are substantially spherical having a van der Waals radii substantially equal to the van der Waals radius of hydrogen;82;f;3 0 The metred according to claim 27 wnerein the step cf mutating said receptor genotype includes at least one of the following steps 1) deletion wnerein a character from tne receptor genotype is deleted, nj duplication where-*1 a character m the receptor genotype is duplicated, n_i _rversicn whereir tr.e sequential order of one or mere characters m the receptor genotype is reversed, ana ivi insertion wherein a cnaracter from the receptor genotype is inserted at a different position m the genotype 31 The method according to claim 3 0 wherein the step cf mutating said receptor genotypes includes recombming randomly selected pairs of said retained mutated receptor genotypes •//heresy corresponding characters m said receptor linear sequences are mtercr.anged 3 2 A method running on a computer for designing simulated receptors mimicking oiological receptors exhibit_ng selective affinity for compounds witn similar functional characteristics, comprising tne strips of a) producing a simulated receptor genotype by generating a receptor linear character sequence wmch codes for spatial occupancy ana charge, b) decoding the genotype to produce a receptor pnenotype, provid_ng a set of target molecules sharing similar functional characteristics, calculating an affinity between the receptor and each target molecule in a plurality of orientations using an effective affinity calculat_on, calculating a sum and maximal affinity between each target molecule and receptor, calculating a sum affinity correlation coefficient for sum affinity versus a functional characteristic for each target molecule and a maximal affinity correlation coefficient for maximal affinity versus said functional characteristic for each target molecule, and calculating a fitness coefficient dependent on said sum and 83 v 1 maximal affinity correlation coef f lcients for each target; molecule, and c) mutating t~e genotype and repeat_rg seep o) and retaining and mutat_rg those receptors exhibiting mcreasea fitness coefficients until a population of receptors witn preselectea fitness coefficients are obtained 33 The metnod according to claim 32 wherei" eacn character m the receptor linear character sequence specifies one of either a spatial turning instruction and a charged site with no turn 34 The method according to claim 33 wherei- said receptor phenotype comprises a plurality cf linear pclymers provided witn a plurality of subunits, each linear pclymer joeing coded for hy a corresponding linear character sequence, one of said subunits being a first sununit m said at least one linear polymer 35 The metnod according t<5^claim 3 4 wnerein said receptor linear character sequence is decoded using an effective receptor assembly algorithm m which t-rn_ng instructions applied to each subunit subsequent to said first subunit are made relative to an initial position of said first subunit 36 The method according to claim 35 wherein said characters specifying spatial turning instructions code for no turn, right turn, left turn, up turn, down tarn, and wnerein characters specifying cnarge sites code for positively cnarged site with no turn, and negatively charged site with no turn 3 7 The method according to claim 3 6 wherein said subunits are substantially spherical having a Van der Waals 84 amende:' radii sue star.c-.al_. eo„al cc the Van der Waals radus of hydrogen 3 3 The meCrca acccra.ng to claim 35 wherein tne step cf mutating sa_o receptor genotype includes at least one of ire following steps i aelet_or. wherein a cnarac^r from t-e receptor genotype _s deleted, 11) duplication wnerein a cnaracter _r the receptor genotype is dupl_cated, 111) inversion wnerein tne sequential order of one or mere cnaracters m the receptor genot/pe is reversed, snc 1/) insertion wherein a cnaracter from tne receptor genotype is inserted at a different position m the genotype 3 9 The metnod accora_ng to claim 3 8 wnerein the step of mutating said receptor genotypes includes recombm_ng randomly selected pairs of said retained mutated receptor genotypes wherecv corresponding cnaracters m said receptor linear sequences are interchanged 40 The metnod according ttN^claim 33 wnerein said effective affinity calculation comprises two measures, tne first oemg a proximity measure wherein a proportion of uncharged portions cn said simulated receptors being sufficiently close to non-polar regions on said molecular structure to generate effective London dispersion forces is estimated, and the second being tne summed strengths of cnarge-dipole electrostatic force interactions generated between charged portions of said simulated receptor ara dipoles present m said molecular structure 41 The method according to claim 40 wherein said sum affinity correlation coefficient is r^2, said maximal affinity correlation coefficient is r^2, and wherein said fitness coefficient is F= (rSA2 x r^2)05, and wherein said preselected fitness coefficient is substantially unity 85 33233 ^3 The method according to claim 19 wherein the receptor linear character sequence additionally codes for relative atomic position and bond type for each atom of said receptor to define a unique three dimensional conformation of said receptor, and wherein said at least one target molecule is represented as a linear character sequence that encodes for spatial occupancy, charge, relative atomic position and bond type for each atom of the target molecule to define a unique three dimensional conformation of the target molecule 44 The method according to claim 43 wherein said linear character sequence for said at least one target molecule comprises a plurality of sequential character triplets, a first character of said triplet being selected from a first character set specifying position and identity of an occupying atom in a molecular skeleton of said target molecule, a second character of said triplet being selected from a second character set specifying the identity of a substituent group attached to said occupying atom, and a third character of said triplet being selected from a third character set specifying the location of said substituent on the atom specified by sad first character of the triplet 86 33233 A ^15 The method according to claim 44 wherein the linear character sequences coding for the target molecules are decoded using an effective molecular assembly algorithm which sequentially translates each triplet from said linear character sequence and thereafter fills unfilled positions on said molecular skeleton with preselected atoms 46 The method according to claim 32 wherein the receptor linear character sequence additionally codes for relative atomic position and bond type for each atom of said receptor to define a unique three dimensional conformation of said receptor, and wherein each target molecule of said set of target molecules is represented as a linear character sequence that encodes for spatial occupancy, charge, relative atomic position and bond type for each atom of the target molecule to define a unique three dimensional conformation of the target molecule 47 The method according to claim 4 6 wherein said linear character sequence for each target molecule comprises a plurality of sequential character triplets, a first character of said triplet being selected from a first character set specifying position and identity of an occupying atom m a molecular skeleton of said target molecule, a second character of said triplet being selected from a second character set specifying the identity of a substituent group attached to said occupying atom, and a third character of said triplet being selected from a third character set specifying the location of said substituent on the atom specified by sad first character of the triplet 87 33233 ^ The method according to claim 4 7 wherein the linear character sequences coding for the target molecules are decoded using an effective molecular assembly algorithm which sequentially translates each triplet from said linear character sequence and thereafter fills unfilled positions on said molecular skeleton with preselected atoms 49 The method according to claim 2 wherein the receptor linear character sequence additionally codes for relative atomic position and bond type for each atom of said receptor to define a unique three dimensional conformation of said receptor, and wherein the molecular linear character sequence additionally codes for relative atomic position and bond type for each atom of said chemical structure to define a unique three dimensional conformation of said chemical structure 50 The method according to claim 1 including the step of storing said linear character sequences for said receptors and said chemical structures m a storage means accessible by a computer 51 The method according to claim 1 wherein a representation of the computationally evolved chemical structures having at least the preselected affinity score are output 52 The method according to claim 19 including the step of storing said linear character sequences for said population of receptors with the preselected fitness coefficients in a storage means accessible by a computer 88 332332 53 The method according to claim 19 wherein a representation of the target molecules having a desirable level of functional activity for said population of receptors are output 54 The method according to claim 32 including the step of storing said linear character sequences for said population of receptors with the preselected fitness coefficients in a storage means accessible by a computer 55 The method according to claim 19 wherein said functional characteristic is biological toxicity 56 The method according to claim 19 wherein said functional characteristic is catalytic activity 57 A method as claimed in claim 1 substantially as herein described with reference to any of Figures 1 to 4c of the accompanying drawings 89