CN110534163A - Using the method for the Octanol/water Partition Coefficients of multi-parameter linear free energy relationship model prediction organic compound - Google Patents
Using the method for the Octanol/water Partition Coefficients of multi-parameter linear free energy relationship model prediction organic compound Download PDFInfo
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Abstract
The invention discloses the methods of the Octanol/water Partition Coefficients using multi-parameter linear free energy relationship model prediction organic compound.On the basis of known organic molecular structure of compounds, only there is by calculating the molecular descriptor of structure feature, using constructed pp-LFER model, it can fast and efficiently predict the Octanol/water Partition Coefficients of organic compound, this method is simple and fast, at low cost, and can save experiment test required human and material resources and financial resources.The present invention is modeled according to the Organization of Economy and Cooperation Development about the building of QSAR model and using directive/guide, with simple, transparent multiple linear regression analysis method, be should be readily appreciated that and is applied；With specific application domain, good capability of fitting, robustness and predictive ability, it can effectively predict the Octanol/water Partition Coefficients of organic compound in application domain, necessary basic data is provided for the Ecological risk assessment of compound and management, is had great importance.
Description
Technical field
The invention belongs to the high-throughput Test Strategy fields of chemicals ecological risk assessment, using multi-parameter linear free energy
The Octanol/water Partition Coefficients of relational model prediction organic compound.
Background technique
Octanol/water Partition Coefficients (Octanol/Water Partition Coefficient, Kow) be defined as it is a certain organic
Object is at a temperature of some, is that characterization has in the ratio of the concentration of two-phase after n-octyl alcohol phase and water phase reach partition equilibrium
The extremely important parameter of machine pollutant partition equilibrium in surrounding medium (water, soil or deposit).K is pointed out in national standardowIt can
Bioaccumulation for evaluating chemical product.Furthermore pass through the K of compoundowCompound can be extrapolated at soil (or deposit)
And the partitions behavior such as distribution coefficient between water phase, help to understand distribution condition of the compound in each environment phase, into
One step makes assessment to processes such as the hydrolysis, photodissociation and biodegrade of compound, has weight to the ecological risk of evaluation compound
Want meaning.
Since QSAR technology helps to realize " principle of precautionary " of poisonous and harmful Pollution by Chemicals management, can reduce
Or substitution related experiment, it makes up the missing of experimental data, reduce experimental expenses, in the poisonous and harmful chemicals ecology wind in countries in the world
Dangerous evaluation and management aspect have obtained extensive exploitation.2004 the Organization of Economy and Cooperation Development (OECD) formally determined
QSAR model development and the directive/guide used, it is specific as follows: (1) that there is clearly defined environmental index；(2) there is specific calculate
Method；(3) application domain of model is defined；(4) there are degree of fitting appropriate, stability and predictive ability；(5) it is preferably able to carry out machine
Understanding is released.
Up to now, have many researcher's application technologies and be successfully established organic compound KowThe prediction model of value.
Such as the EPI Suite of Environmental Protection AgencyTMCarry out predictive compound logK using molecular fragment method in softwareow.For fairly simple
The application condition of compound, this method is small, about in ± 0.12log unit；It however include being permitted for more complicated compound
The error of more pesticides and drug, method may bigger, logKowPerhaps, the error of > 6 substance can be more than a log unit；And
This method can not identify molecule steric-configuration to KowInfluence, can not identify K between isomerowDifference；Document
The chromatographic retention of compound is determined in " J.Pharmaceut.Biomed., 1995,13:339-351. ", and uses KowInto
It has gone linear regression, has established compound chromatographic retention and KowLinear model.However this method can only be predicted
logKowCompound within the scope of 0-6, application domain is smaller and this method can not get rid of experiment limitation, therefore should not use.Document
" J.Phys.Chem., 1988,92:52444-5255 ", " J.Pharm.Sci., 1986,75:629-636 " has collected totally 348 kinds
Compound establishes K by multi-parameter linear free energy methodowPrediction model, model related coefficient is up to 0.99, but more
Parameter linearly still relies on measuring by the Abraham parameter in energy relational model, and the model based on such method is only fitted
For aromatic series and aliphatic compound, application domain is limited, is not suitable for straight chain compound.To sum up, forefathers' model have can not
Thoroughly get rid of that experiment, application domain is small, can not consider the disadvantages of spatial configuration and above-mentioned model is not completely in view of OECD
Requirements in directive/guide lack model verifying and characterization, therefore, it is necessary to construct the classes of compounds that a data set is covered
It enriches, with clear algorithm, the K for being convenient for application and promotion, not depending on experimental data, fully considering molecule steric-configurationowPredict mould
Type, and model is verified and characterized according to OECD directive/guide.
Summary of the invention
Object of the present invention is to develop it is a kind of efficiently, quickly, the succinct and organic compound K that has a wide range of applicationowThe prediction of value
Method.This method can be directly according to required Asia in the Molecular structure prediction multi-parameter linear free energy relationship of organic compound
Bai Lahan parameter value (E, S, A, B, V) and then prediction chemicals KowValue.KowIt is the most basic parameter for studying organic matter distribution, Kow
Facilitate we have appreciated that distribution and biology intracorporal enrichment of the compound in environment items, and KowIt can be used for
Predict chemical toxicity.As the basic environment chemical property of chemicals, KowValue is the Ecological risk assessment and pipe of compound
Reason provides the foundation.Thus exploitation calculation method is used for quick obtaining KowBe conducive to policymaker and manager formulates chemical discharge
Relevant criterion is also advantageous for providing new approaches for environmental improvement.
Technical scheme is as follows:
A kind of side of the Octanol/water Partition Coefficients using multi-parameter linear free energy relationship model prediction organic compound
Method, steps are as follows:
Firstly, proposed adoption multi-parameter linear free energy relationship carries out KOWPrediction, multi-parameter linear free energy relationship is as follows:
KOW=eE+sS+aA+bB+vV+c
Wherein: e, s, a, b, v are model coefficient, are obtained by linear regression fit；E is excess molecular Lorentz-Lorenz molar refraction；S
It is molecular polarity/dipole moment parameter；A and B characterizes molecule hydrogen bond proton donor ability, the ginseng of hydrogen bond proton acceptor ability respectively
Number, also referred to as hydrogen bond acidity and hydrogen bond basicity；V is McGowan molecular volume, and E, S, A, B, V is also known as Abraham parameter；
It needs first to obtain Abraham parameter using multi-parameter linear free energy relationship model, that is, obtains E, the S of compound,
A, B, V value；The 3D structure that compound is first obtained to this, then molecule 3D structure is imported in Dragon 6.0 obtain a large amount of molecules
Structured descriptor, Abraham the parameter value E, S, A, B, V obtained using stepwise multiple linear regression；
Then, using the logK for 683 kinds of organic compounds being collected intoowValue is modeled, and wherein organic compound includes
Organic acid, ester, ether, ketone, alcohol, phenol, aniline, polycyclic aromatic hydrocarbon and its substituent, heterocyclic compound and its derivative, alkyl halide
Hydrocarbon, alkenyl halide；By the logK of 683 kinds of above-mentioned organic compoundsowValue is divided into training set and verifying collection, and training set includes
547 kinds of organic compounds, verifying collection include 136 kinds of organic compounds；Organic compound in training set is tested for constructing model
The organic compound that card is concentrated is for the external certificate after model construction；Internal verification is carried out using bootstrap model；
Descriptor used in above-mentioned model is Abraham descriptor, to the organic compound in training set
logKowValue carries out regression analysis, and the linear relation for obtaining model is as follows:
logKow=0.694 × E -0.866 × S -0.817 × A -3.645 × B+3.689 × V+0.178 (1)
Square R of the related coefficient of model2It is 0.921, root-mean-square error RMSE is 0.568, and it is preferable to illustrate that model has
Capability of fitting；The cross-validation coefficient Q of model2 BootIt is 0.981, illustrates that the robustness of model is preferable；External certificate is determined
Determine coefficient Q2 extIt is 0.922, verifies the root-mean-square error RMSE of collectionextIt is 0.582, shows that the model has good outside pre-
Survey ability.Relatively existing prediction model, the application domain of the model is wider, covers a plurality of types of compounds.These descriptions
Symbol can be described to calculate and be obtained with Dragon, and independent of experimental data, and algorithm is transparent, convenient for the application of model.
It is characterized using application domain of the Williams figure to model.As the Hat (h of compoundi) value be greater than warning value (h*)
When, illustrate compound not in model application domain.hiAnd h*It is calculated by following formula:
hi=xi T(XTX)-1xi (2)
h*=3 (k+1)/n (3)
Wherein xiIt is the descriptor matrix of i-th of compound；xi TIt is xiTransposed matrix；X is the description of all compounds
Accord with matrix；XTIt is the transposed matrix of X；(XTX)-1It is matrix XTX's is inverse；K is the number of variable in model.The h of model*For
0.079, therefore, which is suitable for hiCompound logK less than 0.033owThe prediction of value.
The beneficial effects of the invention are as follows can pass through molecular characterization quick predict organic compound using the method for the present invention
The logK of objectowValue.This method is not only simple and fast, low in cost, but also human and material resources and wealth needed for saving measuring
Power.LogK of the present inventionowThe foundation and verifying of prediction technique are strictly according to QSAR model development and use as defined in OECD
Therefore directive/guide uses the logK of the patent of inventionowPrediction result, can for chemicals supervise provide data support, to chemistry
The Ecological risk assessment of product is of great significance.
Method provided by the invention has a characteristic that
(1) modeling process uses transparent algorithm-MLR, in addition, 5 descriptors are used for the building of prediction model, model
Succinctly, it is easy to explain, is convenient for application and promotion；
(2) application domain of model is wide, cover chlordiphenyl, phthalate ester, polycyclic aromatic hydrocarbon and its substituent, heterocyclic compound and its
The multiclass compound such as derivative, halogenated alkane, alkenyl halide, organic acid, ester, ether, ketone, alcohol, phenol, aniline, can be used for predicting
The logK of variety classes organic compoundowValue provides data and supports for the Ecological risk assessment of chemicals and supervision；
(3) modeling process strictly about the building of QSAR model and uses directive/guide according to OECD, and model built has good
Capability of fitting, robustness and predictive ability.
(4) model is different by the method for experiment value from forefathers completely by the way of calculating, and can largely reduce experiment
Cost, more efficient acquisition chemicals KowValue.
Detailed description of the invention
Fig. 1 is training set logKowMeasured value and predicted value fitted figure, training set compound be 547 kinds.
Fig. 2 is verifying collection logKowMeasured value and predicted value fitted figure, verifying collection compound be 136 kinds.
Fig. 3 is that the Williams of model schemes, and red indicates that training set compound, black indicate verifying collection compound, warning
Value h*It is 0.033.
Specific embodiment
Embodiment 1
A compound paranitrochlorobenzene (No. CAS: 100-00-5) is given, to predict its logKowValue.First according to nitre
The Smiles code of base chlorobenzene carries out structure optimization to it using Openbabel software, the 3D structure based on optimization, uses
6.0 software of Draogon calculates the respective value of descriptor, substitutes into formula and E, S, A is calculated, and B, V are respectively 0.956,
1.246,0.060,0.336,1.009.It is 0.007 (< 0.033) according to the h value that formula (2) calculate, so the compound is in mould
In type application domain, the value of above description symbol is substituted into formula (1), obtains logKowPredicted value be 2.21, measuring
logKowValue is 2.39, and predicted value is consistent very much with the data of experiment value.
Embodiment 2
A compound 4- nitroaniline (No. CAS: 100-01-6) is given, to predict its logKowValue.First according to 4- nitre
The Smiles code of base aniline carries out structure optimization to it using Openbabel software, the 3D structure based on optimization, uses
6.0 software of Draogon calculates the respective value of descriptor, substitutes into formula and E, S, A is calculated, and B, V are respectively 1.117,
1.564,0.294,0.542,1.026.It is 0.011 (< 0.033) according to the h value that formula (2) calculate, so the compound is in mould
In type application domain, the value of above description symbol is substituted into formula (1), obtains logKowPredicted value be 1.17, measuring
logKowValue is 1.39, and predicted value is consistent very much with the data of experiment value.
Embodiment 3
A compound ethylo benzene (No. CAS: 100-41-4) is given, to predict its logKowValue.First according to 4- nitrobenzene
The Smiles code of amine carries out structure optimization to it using Openbabel software, the 3D structure based on optimization, using Draogon
6.0 softwares calculate the respective value of descriptor, substitute into formula and E, S, A is calculated, and B, V are respectively 0.621, and 0.527 ,-
0.003,0.218,1.036.It is 0.003 (< 0.033) according to the h value that formula (2) calculate, so the compound is in model application
In domain, the value of above description symbol is substituted into formula (1), obtains logKowPredicted value be 3.18, the logK of measuringowValue
It is 3.15, predicted value is consistent very much with the data of experiment value.
Claims (2)
1. a kind of method of the Octanol/water Partition Coefficients using multi-parameter linear free energy relationship model prediction organic compound,
It is characterized in that, steps are as follows:
Firstly, proposed adoption multi-parameter linear free energy relationship carries out KOWPrediction, multi-parameter linear free energy relationship is as follows:
KOW=eE+sS+aA+bB+vV+c
Wherein: e, s, a, b, v are model coefficient, are obtained by linear regression fit；E is excess molecular Lorentz-Lorenz molar refraction；S is point
Sub- polarity/dipole moment parameter；A and B characterizes molecule hydrogen bond proton donor ability, the parameter of hydrogen bond proton acceptor ability respectively,
Referred to as hydrogen bond acidity and hydrogen bond basicity；V is McGowan molecular volume, and E, S, A, B, V is also known as Abraham parameter；
It needs first to obtain Abraham parameter using multi-parameter linear free energy relationship model, that is, obtains E, S, A, the B of compound,
V value；The 3D structure that compound is first obtained to this, then molecule 3D structure is imported in Dragon 6.0 obtain a large amount of molecular structures
Descriptor, Abraham the parameter value E, S, A, B, V obtained using stepwise multiple linear regression；
Then, using the logK for 683 kinds of organic compounds being collected intoowValue is modeled；By 683 kinds of above-mentioned organic compounds
LogKowValue is divided into training set and verifying collection, and training set includes 547 kinds of organic compounds, and verifying collection includes 136 kinds and organises
Close object；Organic compound in training set verifies the organic compound of concentration for outer after model construction for constructing model
Portion's verifying；Internal verification is carried out using bootstrap model；
Descriptor used in above-mentioned model is Abraham descriptor, to the logK of the organic compound in training setowValue
Regression analysis is carried out, the linear relation for obtaining model is as follows:
logKow=0.694 × E -0.866 × S -0.817 × A -3.645 × B+3.689 × V+0.178 (1).
2. the octanol/water according to claim 1 using multi-parameter linear free energy relationship model prediction organic compound
The method of distribution coefficient, which is characterized in that the organic compound includes organic acid, ester, ether, ketone, alcohol, phenol, aniline, more
Cycloaromatics and its substituent, heterocyclic compound and its derivative, halogenated alkane, alkenyl halide.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111564185A (en) * | 2020-03-19 | 2020-08-21 | 浙江师范大学 | Method for rapidly predicting distribution coefficient of stored fat/water of organic compound |
| CN111768812A (en) * | 2020-07-07 | 2020-10-13 | 扬州大学 | Method for predicting organic PDMS film-water distribution coefficient |
| CN111768814A (en) * | 2020-07-07 | 2020-10-13 | 扬州大学 | Method for predicting POM-water distribution coefficient of organic pollutant based on quantitative structure-activity relationship |
| CN111768815A (en) * | 2020-07-07 | 2020-10-13 | 扬州大学 | Method for predicting distribution coefficient of POPs (Point-of-sale) in PUF (physical unclonable function) membrane-air based on theoretical linear solvation energy relation model |
| CN112086141A (en) * | 2020-09-09 | 2020-12-15 | 扬州大学 | Method for predicting PA-water distribution coefficient of organic pollutant based on quantitative structure property relation |
| CN113591394A (en) * | 2021-08-11 | 2021-11-02 | 清华大学 | Method for predicting organic compound n-hexadecane/air distribution coefficient |
| CN113689916A (en) * | 2021-09-02 | 2021-11-23 | 赣南师范大学 | Universal model for predicting organic compound performance and prediction method |
| CN113705008A (en) * | 2021-08-31 | 2021-11-26 | 扬州大学 | Prediction model, modeling method and prediction method for distribution coefficient of POPs between XAD films and air |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040225451A1 (en) * | 2003-03-20 | 2004-11-11 | Anton Filikov | Method of prediction of solubility of chemical compounds |
| CN102999705A (en) * | 2012-11-30 | 2013-03-27 | 大连理工大学 | Method for predicting n-octyl alcohol air distribution coefficient (KOA) at different temperatures through quantitative structure-activity relationship and solvent model |
| CN103488901A (en) * | 2013-09-25 | 2014-01-01 | 大连理工大学 | Method for adopting quantitative structure-activity relationship model to predicting soil or sediment adsorption coefficients of organic compound |
-
2019
- 2019-08-22 CN CN201910780278.8A patent/CN110534163B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040225451A1 (en) * | 2003-03-20 | 2004-11-11 | Anton Filikov | Method of prediction of solubility of chemical compounds |
| CN102999705A (en) * | 2012-11-30 | 2013-03-27 | 大连理工大学 | Method for predicting n-octyl alcohol air distribution coefficient (KOA) at different temperatures through quantitative structure-activity relationship and solvent model |
| CN103488901A (en) * | 2013-09-25 | 2014-01-01 | 大连理工大学 | Method for adopting quantitative structure-activity relationship model to predicting soil or sediment adsorption coefficients of organic compound |
Non-Patent Citations (4)
| Title |
|---|
| ZHU TENGYI 等: "Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models", 《ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY》 * |
| 张良静 等: "含特殊官能团有机物分配系数预测的多参数线性自由能关系（PP-LFERs）研究", 《环境化学》 * |
| 肖子君 等: "多参数线性自由能关系（pp-LFER）模型中分子结构参数的预测方法", 《第二次全国计算毒理学学术会议暨中国毒理学会第一届计算毒理专业委员会第二次会议》 * |
| 顾敬梓: "部分除草剂正辛醇/水分配系数的QSPR研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
Cited By (10)
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| CN111564185A (en) * | 2020-03-19 | 2020-08-21 | 浙江师范大学 | Method for rapidly predicting distribution coefficient of stored fat/water of organic compound |
| CN111768812A (en) * | 2020-07-07 | 2020-10-13 | 扬州大学 | Method for predicting organic PDMS film-water distribution coefficient |
| CN111768814A (en) * | 2020-07-07 | 2020-10-13 | 扬州大学 | Method for predicting POM-water distribution coefficient of organic pollutant based on quantitative structure-activity relationship |
| CN111768815A (en) * | 2020-07-07 | 2020-10-13 | 扬州大学 | Method for predicting distribution coefficient of POPs (Point-of-sale) in PUF (physical unclonable function) membrane-air based on theoretical linear solvation energy relation model |
| CN112086141A (en) * | 2020-09-09 | 2020-12-15 | 扬州大学 | Method for predicting PA-water distribution coefficient of organic pollutant based on quantitative structure property relation |
| CN113591394A (en) * | 2021-08-11 | 2021-11-02 | 清华大学 | Method for predicting organic compound n-hexadecane/air distribution coefficient |
| CN113591394B (en) * | 2021-08-11 | 2024-02-23 | 清华大学 | Method for predicting n-hexadecane/air distribution coefficient of organic compound |
| CN113705008A (en) * | 2021-08-31 | 2021-11-26 | 扬州大学 | Prediction model, modeling method and prediction method for distribution coefficient of POPs between XAD films and air |
| CN113689916A (en) * | 2021-09-02 | 2021-11-23 | 赣南师范大学 | Universal model for predicting organic compound performance and prediction method |
| CN113689916B (en) * | 2021-09-02 | 2024-04-16 | 赣南师范大学 | General model for predicting performance of organic compound and prediction method |
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