KR101295865B1 - Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Octanol-Water Partition Coefficient of Pure Organic Compound - Google Patents

Abstract

The present invention consists of up to five elements such as hydrogen (H), carbon (C), nitrogen (N), oxygen (O), and sulfur (S), and is composed of pure molecules consisting of up to 25 atoms except hydrogen. A mathematical model for predicting the Octanol-Water Partition Coefficient of organic compounds with high accuracy is provided. The model shows that, for a large number of organic compounds that satisfy the above conditions for which the experimental values of the octanol-water partition coefficient are known, some of the various molecular descriptors are independent variables and octanol-water partitions. Of the many polylinear regression models whose coefficients are dependent variables, the best is obtained by using a genetic algorithm, and the octanol-water partition coefficient is output by inputting the values of molecular descriptors included in this model. As a multiple linear regression-artificial neural network hybrid model that further improves predictive performance by constructing an artificial neural network, which is an example of a QSPR model and only the specific values of molecular presenters included in the model are known, Any molecule predicts the octanol-water partition coefficient of a purely composed compound. As such, the present invention provides a method for predicting the value of the octanol-water partition coefficient that is reliable even for a large number of organic compounds in the above-mentioned conditions, in which the experimental value is unknown. It produces effects such as facilitating research and development activities.

Description

Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Octanol-Water Partition Coefficient of Pure Organic Compound}

The present invention belongs to a field of physical chemistry called physical property prediction and relates to a method for predicting with high accuracy the octanol-water partition coefficient, which is one of several physical properties of an organic compound.

Knowing the exact values of the various properties of an organic compound in detail can be used to determine the feasibility of the use of the substance, to design the synthesis and purification processes, and to establish methods and conditions for storage, transport, use, and disposal. It is an important issue both industrially and academically because it is critical to all decision-making processes throughout the process. The most accurate way of knowing the value of the property of interest of an organic compound of interest is also an experiment, but it is true that it is quite costly and time consuming in many aspects, including the preparation of purified samples and the construction of an environment for accurate measurement. Therefore, it may not be possible. Therefore, as an alternative, many researchers have long tried to predict the exact value of various physical properties of organic compounds. As such, the prediction of physical properties has a long history, and new prediction methods are constantly appearing. At present, several prediction models with different accuracy and application range coexist.

Several prediction models have been proposed to date for the octanol-water partition coefficient, which is the property of interest of the present invention. Octanol-Water Partition Coefficient refers to a coefficient representing the distribution of solutes in two unmixed materials, octanol and water. The results of previous studies on the prediction of octanol-water partition coefficients are described in R. Mannhold, H. Kubinyi, G. Folkers, Molecular Drug Properties: Measurement and Prediction . Wiley-VCH. (2008).]. Currently known and widely used models for predicting octanol-water partition coefficients are mainly those using group contribution methods. Table 1 shows the programs and providers that calculate the major group contribution models for the octanol-water bonsai coefficients that have been proposed.

program Provider KLOGP MULTICASE ENWWIN Syracuse Research Corporation US EPA CLOGP Daylight Biobyte ACD / LogP Advanced Chemistry Development AB / LogP Advanced Pharma Algorithms MOLCAD Tripos TSAR Accelrys PROLOGP Compudrug ALOGP97 in Cerius Accelrys XLOGP2.0 Luhua lai

A typical form of group contribution model is given by

를 구하기 위해서는 먼저 값을 알고자 하는 화합물의 분자를 미리 정해진 다수의 조각형식들에 맞추어 쪼갠 다음 각 조각형식들의 개수

를 구한다. 이를 다시 그 형식에 할당된 계수

와 곱한 것을 합산한 것이 예측값

가 된다. 계수

들은 실험값이 존재하는 화합물들로부터 모형이 최선의 성능을 갖도록 통계적인 방법을 통해 결정된다.Property value

In order to find the value, first divide the molecule of the compound whose value you want to know according to a number of pieces.

. This is again the coefficient assigned to that type

Multiplying and multiplied by

. Coefficient

These are determined by statistical methods to ensure that the model has the best performance from the compounds with experimental values.

These group contribution methods have been somewhat successful in the past, but lack the theoretical basis, and sometimes the only way to split them into pieces is not, or even nonexistent, which makes the calculation of values impossible. Also, as the model is improved to improve the predictive performance, it becomes more complicated and difficult to handle.

One of the alternatives to the group contribution method in constructing the prediction model is the QSPR (quantitative structure-property relationship) method. This method basically starts with the assumption that the properties of a compound are a function of the structural properties of the molecule and uses various molecular descriptors that reflect different structural properties. The number of molecular descriptors proposed to date has been thousands, and many kinds of molecular descriptors range from simple ones such as the number of carbons or hydrogens in a molecule to complex ones such as the shape, connection state, and electrochemical properties of molecules. Calculation methods have been developed [Todeschini R., V. Consonni V., Molecular Descriptors for Chemoinformatics: Second, Revised and Enlarged Edition: Volume I / II , Wiley-VCH, 2009]. The QSPR prediction model is presented in the form of a function that includes some of these molecular descriptors and sometimes, in addition, some of the other physicochemical properties of the compound, which are also functions of structural properties.

들의 선형 결합 함수이며 각 계수

들은 주로 다중선형회귀분석을 통해 실험데이터로부터 결정된다.The most frequently adopted form of such a function is

Linear coupling function of each coefficient

These are mainly determined from experimental data through multiple linear regression analysis.

이 부여되어 있다. 은닉층과 출력층의 각 노드들은 전단계의 노드들로부터 이러한 연결들을 통해 입력을 받은 뒤 이를 가공하여 출력값을 만드는데 이때 활성화 함수(activation function)라 불리는 함수

를 적용한다. 이러한 인공신경망을 실제로 활용하려면 먼저, 다양한 입력값과 그 입력값에 대응하는 출력값을 함께 묶어 놓은 샘플집합을 이용하여 인공신경망을 훈련시키는 과정이 필요한데 이는 주어진 입력에 대한 인공신경망의 출력과 원하는 출력의 차이가 최소가 되도록 역전파(back propagation) 알고리즘을 사용하여 각 연결의 가중치를 최적화 하는 것을 말한다. 이러한 훈련을 거친 인공신경망은 문제해결에 필요한 규칙이나 지식을 따로 제공하지 않아도 학습을 통해서 스스로 일반적인 규칙을 수립하여 미지의 입력에 대해서도 타당성 있는 출력을 내주므로 화합물의 물성예측과 같이 기반 이론이 결여되어 있는 분야에 매우 유용한 수단으로 널리 이용되고 있다.Another way to create a QSPR model is to use an artificial neural network. Artificial neural network technology is one of the long-standing research results of human beings who want to make intelligent machines by modeling human nerve cells with intelligence, and is an information processing technology that has been applied in various fields since the mid 20th century. . 2 shows a typical example of an artificial neural network. As can be seen, the artificial neural network has an input layer for receiving input data, an output layer for producing output data, and a hidden layer located between them, each layer having one or more nodes. ) Each node in the hidden layer is connected to nodes in the input and output layers, and each connection has a quantity called a weight.

Is granted. Each node in the hidden and output layers receives inputs from these nodes and processes them to produce output values. This is called an activation function.

Is applied. In order to actually use such an artificial neural network, first, the neural network is trained using a sample set in which various input values and output values corresponding to the input values are bundled together. This means optimizing the weight of each connection using a back propagation algorithm to minimize the difference. The neural network that has undergone such training does not provide the necessary rules or knowledge to solve the problem, but it establishes general rules through learning and gives valid output for unknown inputs. It is widely used as a very useful means in the field.

The QSPR prediction model for predicting octanol-water partition coefficient using an artificial neural network is not very proposed, and the proposed ones also have disadvantages in that the number of sample compounds is not large or the application target is limited to a specific series of compounds. Bernd Beck, Andreas Breindl, and Timothy Clark. QM / NN QSPR Models with Error Estimation: Vapor Pressure and Log P. J. Chem. Inf. Comput. Sci. 2000, 40, 1046-1051 report a model using 16 molecular descriptors using data for 1085 compounds. Afterwards, a research using an artificial neural network has been conducted to report a network layer [16-10-1] having a coefficient of determination of 0.74.

The technical problem to be solved by the present invention is to overcome the shortcomings of the existing models mentioned above and to show more broad and more accurate prediction performance, hydrogen (H), carbon (C), nitrogen (N), oxygen (O), sulfur ( A QSPR model is constructed for the octanol-water partition coefficient of pure organic compounds consisting of up to 5 elements, including S) and molecules with 25 or less atoms except hydrogen.

We have achieved this goal by constructing a multiple linear regression-artificial neural network hybrid model that takes into account a wider variety of molecular presenters based on more experimental data. In particular, the neural network has the advantage of reflecting the nonlinear functional relationship between independent and dependent variables, which cannot be reflected by the multiple linear regression model, so that a model with higher predictive performance can be realized. However, the artificial neural network has a disadvantage in that its stability is lower than that of the multiple linear regression model due to its high degree of freedom in rule setting. In the present invention, when the prediction value of the artificial neural network model and the prediction value of the multiple linear regression model show a large difference, the method of adopting the prediction value of the multiple linear regression model compensates for these shortcomings. We established an excellent prediction model that takes advantage of the neural network model.

The above-mentioned limitations on the range of organic compounds to which the predictive model can be applied are mainly due to the current state of the art when some of the molecular descriptors used require quantum mechanical calculations. This is due to the fact that for the compounds beyond the stated range, problems arise in terms of accuracy and calculation time. However, even within the above limitations, since there are a great many compounds and industrially important compounds are included in a large amount, it is determined that the present invention can greatly benefit human society.

Humans today depend on a wide variety of organic compounds, including plastics, fibers, rubber, paints, fertilizers, medicines and fuels, and this trend is expected to intensify. According to the American Chemical Society (ACS), as of July 2010, the total number of compounds registered was over 54,000,000. In comparison, even if the physical property value is only one, the number of experimentally known compounds is only tens of thousands. The physical property value of compounds is essential for the better life of mankind, including the development of new materials and new drugs, the optimal design of chemical plants, the improvement of the productivity of existing facilities, the development and saving of resources, the securing of safety, and the protection of the environment. In particular, the octanol-water partition coefficient is a very important property in the development of new drugs, and international regulations on the production and consumption of compounds such as REACH may require them. However, at present, the number of compounds whose experimental values are known is at most tens of thousands, and depending on the compounds, the work of obtaining data through experiments may be past due to toxicity, instability and difficulty of purification. From this point of view, the present invention, which can obtain the octanol-water partition coefficient value of a large number of compounds with high accuracy without any experiment, not only saves the cost and time required for the experiment, It makes it possible to make researches and development activities of related industries easier, and furthermore, to provide appropriate information to all places that need such values, such as academia and the academic world, to make the activities more smoothly. I will give birth.

1 is a flowchart illustrating a process of constructing a multiple linear regression-artificial neural network hybrid model for the octanol-water partition coefficient provided by the present invention.
Figure 2 is a view showing the structure of the artificial neural network used in the present invention.
3 is a parity diagram of 807 experimental data of a model using UNIFAC, which is a group contribution model for octanol-water partition coefficient.
4 is a parity plot of 1240 experimental data of a multiple linear regression-artificial neural network hybrid model for octanol-water partition coefficient.
FIG. 5 is a histogram diagram of 807 experimental data of a model using UNIFAC.
FIG. 6 is a histogram plot of 1240 experimental data of a multiple linear regression-artificial neural network hybrid model.

Figure 1 is a simplified diagram illustrating the process of building a multiple linear regression-artificial neural network hybrid model for the octanol-water partition coefficient.

The first thing to do when building the model is to collect and review the experimental data as specified in step 1. As a result of extensive research on all literatures and data that can be referred to through various papers, books, Internet sites, etc. for the present invention, the octanol-water partition coefficient for a total of 1724 compounds satisfying the conditions of the present invention Data was collected. The collected data were examined in a variety of ways to determine whether they were truly valid values that could be used to build the model.They were not experimental values, errors in data notation, or values for the same compound. After carefully analyzing data such as unreliably distant values or values for molecular descriptors that are difficult to prepare immediately, the data were modified or deleted to finally select data for a total of 1320 compounds. . And in terms of predictive performance, it was better to classify them separately into carbon- and hydrogen-only hydrocarbons and non-hydrocarbons. The model was established by classification into hydrocarbons. In the present invention, the 'organic compound' or 'compound' is composed of five elements such as hydrogen (H), carbon (C), nitrogen (N), oxygen (O), and sulfur (S). Refers to a substance consisting of molecules with 25 or fewer atoms excluded.

The next step is to prepare the values of the molecular descriptors for these compounds. The values for various molecular descriptors totaling up to 1978 are computed in batches using a computer from files containing information on the molecules of each compound. In order to calculate the electronic structure of molecules, the Schrodinger equation is usually solved by a pure theory to solve the electron energy. However, in the case of systems with many electrons, Hartley uses an approximation method that ignores electron correlation. Hartree-Fock (HF) method [CC J. Roothan, Rev. Mod. Phys. 23, 69 (1951)]. This approximation introduces a fundamental error in the calculated results and adds a multidimensional theoretical perturbation term to the Post Hartree-Fock method [C. Moller and M. S. Plesset, Phys. Rev. 46, 618 (1934)], to obtain a more accurate solution, but require a relatively large amount of computation. In this way, it is too costly or time-consuming to calculate large molecules.

In addition, the Gaussian method that combines Hartley-Fork and Post Hartley-Fork [L. A. Curtiss, K. Raghavachari, G. W. Trucks, and J. A. Pople, J. Chem. Phys. 94, 7221 (1991); L. A. Curtiss, K. Raghavachari, P. C. Redfern, V. Rassolov, and J. A. Pople, J. Chem. Phys. 109, 7764 (1998) show very little error in energy prediction, but more computation is required because it performs energy calculations for several post-Hartley-Fork methods.

Density functional theory is used to find the ground state using the function of the total energy using the electron density function instead of the wave function with the multidimensional perturbation term to consider the correlation between the electrons of the molecules of many electrons. R. Seeger and JA Pople, J. Chem. Phys. 66, 3045 (1977). The advantage of the density functional theory is that the electron density only needs to be taken into account so that more accurate results can be obtained with comparable calculations to the Hartree-Fock method. The combination of exchange functions and correlation functions for calculating the exchange-correlation energy of the electrons is used to obtain more improved results without increasing the calculation amount.

The computational theories previously attempted to select an optimal quantum mechanical calculation method are the density ranges of the aforementioned Hartley-Fork method, various Post-Hartley-Fork methods, Gaussian (G2, G3) methods, and various combinations of functional functions. Function theory. Among them, one method of density functional theory, which is the best performance calculation time, was selected.

Therefore, in the present invention, the optimization of the molecular structure and the frequency calculation are performed by applying the calculation method of the specified density functional theory using a commercial quantum mechanical calculation program.

In the optimized structure, not only the above physical information but also molecular descriptors represented by various meaningful values reflecting the characteristics of the molecules can be obtained. Some molecular descriptors can express the characteristics of two-dimensional structures, while others represent the characteristics of three-dimensional structures. Divided into 24 categories, including detailed presenters in each category. After calculating the molecular descriptor values, we picked out those that were not suitable, that is, the values were the same for all sample compounds and could not be independent variables in the model. This is because it prevents irrelevant molecular expressions from being included in the prediction model, thereby increasing the reliability of the model and reducing the computation time required to find the optimal model by reducing the number of molecular expressions.

In step 4, the sample compounds are divided into two parts: the training set used to explore the predictive model, and the test set used to test the predicted performance of the determined model. Sample hydrocarbons and non-hydrocarbons were divided between 5: 5 and 8: 2, preferably 6 to 4, taking care not to distribute similar molecules on one side only.

이며 이들을 다 조사하는 것은 현실적으로 불가능하다.We then find the best multiple linear regression model based on the training set. The term 'best' is used in the sense of relative meaning that it can be obtained in a relatively short time and has a performance very close to the optimal solution in the absolute sense. The reason for not finding the optimal solution directly is because of the long computation time. For example, when there are 1700 suitable molecular representations out of 1978 molecular representations, you can choose from five different linear regression models. The total number is

It is practically impossible to investigate them all.

In order to obtain useful results within a limited time, the present invention provides a genetic algorithm [Judson, "Genetic Algorithms and Their Uses in Chemistry", Reviews in Computational Chemistry, Lipkowitz & Boyd, Eds., Vol. 10, pp. 1 -73 (VCH Publishers, NY, 1997)]. First, a population of multiple linear regression models is created by randomly drawing a certain number of molecular descriptors from a pool of molecular descriptors. For example, let's say we created a population of 1000 different polylinear regression models that were randomly drawn from 5 of the 1700 suitable molecular descriptors.

Individuals, called chromosomes, are coded by combining the numbers of extracted molecular descriptors. For example, the chromosome of the multiple linear regression model formed by the 45th, 167, 684, 1033, and 1502th molecular descriptors among 1700 molecular descriptors can be expressed as (45, 167, 684, 1033, 1502). Two parent chromosomes are selected from the populations thus generated and crossed over to generate children. In the present invention, the Roulette Wheel method is adopted as a selection method for selecting the parent chromosomes.

) 또는 평균절대오차(average absolute error: AAE)를 활용하였다. 즉 결정계수값이 크거나 평균절대오차값이 작은 것이 선택확률이 높도록 하였다.The Roulette Wheel method is the most commonly used selection algorithm, which allocates a roulette region to the chromosome in proportion to the fitness of each chromosome, and then rotates the roulette to select the chromosome of the corresponding region. Therefore, in this method, the higher the fit, the more likely it is to be selected. The coefficient of determination of the regression model is used to calculate the goodness of fit for each chromosome that determines the probability of selection.

) Or average absolute error (AAE). In other words, the larger the coefficient of determination or the smaller the mean absolute error, the higher the probability of selection.

The single point crossover method is adopted as the breeding method. The most common breeding method is to generate a child by selecting one crossing point on the parent chromosome and exchanging chromosomal parts before and after the point. For example, if a parent chromosome is given as (24, 262, 343, 789, 1290), (38, 454, 554, 1322, 1449) and a cross point is placed between the third and fourth elements, then the child chromosomes are (24, 262, 343, 1322, 1449), (38, 454, 554, 789, 1290).

When the offspring are created, they have a chance of mutating a portion of their chromosomes, which randomly replaces several elements with completely new values. This new information does not exist in the current population. It is a process that makes it possible to search to compensate for the case where there is no proper solution in the initial set combination.

In this way, a new generation of populations are created by replacing some or all of the existing populations with newly obtained entities. Repeat this process until the number of generations reaches a pre-determined value (usually between 10 and 1000), and then select and end up the regression model with the best predictive performance.

Once this best multiple linear regression model has been selected, the next step is to examine its validity. If a problem is found that the t-test value of the molecular descriptors included in the model is not good, go back and look for another model. For example, if the number of sample compounds is 1005 and the selected model consists of five molecular descriptors, if the t-test for one of the molecular descriptors is 3.3 or higher, then the probability that the molecular descriptor is irrelevant to that property is 0.1 It means less than%. In the present invention, when there is a molecular presenter having a t-test value of less than about 3, the selected model is discarded and another model is found. In addition, even if the values of the molecular descriptors for the sample compounds are the same except for a few few compounds, they are not considered to be reliable models. In general, increasing the number of molecular expressions included in the model increases the predictive performance, but such problems occur. Therefore, the final model is usually obtained by repeating these steps through several trials and errors while changing the number of molecular expressions. If the problem no longer appears in the selected model, proceed to the next step.

Next, in Step 7, assess the predictive performance of the found model using test sets that did not participate in model formation. If a problem is found in the training set that results in much lower predictive performance or samples that are significantly off predicted, go to step 4 and readjust the training and test sets before proceeding. If the difference between the training set and the test set does not exceed 20% of the absolute mean error (AAE) obtained for the training set, it is judged that the predictive performance is satisfied.

In this way, once the multiple linear regression model for the octanol-water molecular distribution coefficient is established, the first step is to standardize the data of the molecular presenters and the experimental data of the octanol-water molecular distribution coefficient to establish the artificial neural network model. Subtract the mean of the data from the values and divide by the standard deviation. The entire sample thus prepared is divided into a training set, a validation set, and a test set in an approximately 6: 2: 2 ratio.

을, 출력층의 활성화 함수로는 선형함수 즉

를 채택하였다. 따라서 입력층의 각 노드들이 받는 입력값들을

라 할 때 은닉층의 j번째 노드의 출력값은

와 같이 주어지며 은닉층이

개의 노드로 이루어져 있을 때 출력층 출력노드의 최종 출력값은

와 같이 주어진다. 여기서

는 문턱 가중치(threshold weight)를 의미한다.We then use them to find the best artificial neural network model. In this case, the search range is limited to a neural network having a hidden layer between the input layer and the output layer as shown in FIG. 2 and having three structures connected only in a feed forward direction, that is, in a direction from the input to the output. It was. The input layer is composed of the same number of nodes that receive the values of each of the molecular markers included in the already established polylinear regression model, and the output layer is composed of one node that outputs the octanol-water partition coefficient. . In addition, as the activation function of the hidden layer, the Sigmoid function,

, The activation function of the output layer is a linear function

Was adopted. Therefore, the input values that each node in the input layer receives

In this case, the output value of the j th node of the hidden layer is

Is given by

The final output value of the output layer output node when composed of four nodes

As shown in Fig. here

Denotes a threshold weight.

들의 다양한 초기값세트(보통 1000세트이내)를 마련하고, 훈련집합을 사용하여 각 세트로 초기화된 신경망을 역전파 알고리즘을 통해 반복 훈련함으로써 가중치

들의 최적화된 값을 찾는다. 최적화에 대한 판단은 매 훈련 후 경신된 가중치들의 값으로 정해지는 모형을 검증집합에 적용하였을 때 그 평균제곱오차(mean square error)의 값이 최소가 되는 것으로 한다. 보통은 3000~5000번의 반복훈련 내에 이러한 시점이 나오게 된다. 이렇게 얻어진 각 초기값세트에 대응하는 최적화된 신경망모형을 훈련집합, 검증집합, 시험집합에 각각 적용하여 그 평균제곱오차들이 모두 다중선형회귀모형의 그것들보다 작은 것만을 모은다. 이러한 것이 여러 개 있을 경우, 결정계수나 평균절대오차 등을 기준으로 가장 우수한 모형을 선택한다.The search proceeds from increasing the number of hidden nodes to one in order. Usually, the search proceeds to twice the number of input nodes. However, if a satisfactory model is not found, the search proceeds further. The detailed procedure is as follows. First, the weight generated by using random number generation function for each number of hidden nodes

Prepare different sets of initial values (usually within 1000 sets) and weight them by repeatedly training the neural networks initialized with each set using the back-up algorithm.

Find the optimal value for these. The judgment of the optimization is that the mean square error is minimized when the model, which is determined by the updated weights after each training, is applied to the test set. Normally this will occur within 3000 to 5000 repetitions. The optimized neural network model corresponding to each set of initial values thus obtained is applied to the training set, the test set, and the test set, respectively, to collect only those whose mean square errors are smaller than those of the multiple linear regression model. If there are several of these, choose the best model based on the coefficient of determination or the absolute absolute error.

, 비탄화수소의 경우 1

)을 정하여 인공신경망모형과 다중선형회귀모형의 예측값들 차이의 절대값이 기준값을 넘을 경우 다중선형회귀모형의 예측값을 채택하고 이보다 작을 경우 인공신경망모형의 값을 채택하게 하는 것을 말한다.

은 옥탄올과 물에 녹아있는 화합물의 몰비에 log를 취한값을 의미한다.When the artificial neural network model is selected, an overfitting prevention standard is finally set. This is a measure to improve the instability that artificial neural networks give wrong answers to unknown inputs as a result of overtraining, a reasonable threshold (0.3 for hydrocarbons).

, For hydrogenated 1

), Which means that if the absolute value of the difference between the predicted values of the artificial neural network model and the multiple linear regression model exceeds the reference value, the predicted value of the multiple linear regression model is adopted.

Means the logarithm of the molar ratio of the compound dissolved in octanol and water.

The results of establishing a multiple linear regression-artificial neural network hybrid model for the octanol-water partition coefficient are summarized in Tables 2 and 3. In Table 2 (main content of the QSPR predictive model for the hydrocarbon octanol-water partition coefficient) and Table 3 (main content of the QSPR predictive model for the non-hydrocarbon octanol-water partition coefficient), ClogP is abbreviated as Calculated logP. The term P refers to the first letter of the partition coefficient. The ClogP calculation program was calculated using a program provided by Biobyte among the ClogP providers shown in Table 1.

Number of sample compounds 225 Number of molecular descriptors 5 Names of Molecular Presenters P ₁ : Octanol-water partition coefficient obtained by a commercial program (ClogP)
P ₂ : self-returning walk count of order 09
P ₃ : maximum electrotopological negative variation
P ₄ : Number of CHR3 functional groups (CHR3)
P ₅ : Tokyo distribution function-5.5 / atomic van der Waals volume weighting
(Radial Distribution Function-5.5 / weighted by atomic van der Waals volumes) Regression Model Decision Coefficients 0.987408 Regression Model Mean Absolute Error

0.078578

Regression model Octanol-water partition coefficient [

] =

Artificial Neural Network Determination Coefficient 0.99034 Artificial neural network mean absolute error 0.126672

Artificial Neural Network Model Octanol-water partition coefficient

Overconformity Prevention Criteria 0.3

Number of sample compounds 1095 Number of molecular descriptors 7 Names of Molecular Presenters P ₁ : Octanol-water partition coefficient obtained by a commercial program (ClogP)
P ₂ : H autocorrelation of lag 6 / Weighted by atomic masses
P ₃ : Minimum valency of a C atom
P ₄ : Number of Al-NO2 functional groups (Al-NO2)
P ₅ : V Total Size Index / Atomic Polarization Weighting
(V total size index / weighted by atomic polarizability)
P ₆ : number of (aliphatic) nitro groups
P ₇ : number of guanidine derivatives Regression Model Decision Coefficients 0.976299 Regression Model Mean Absolute Error

0.098008

Regression model Octanol-water partition coefficient [

] =

Artificial Neural Network Determination Coefficient 0.978309 Artificial neural network mean absolute error 0.162711

Artificial Neural Network Model Octanol-water partition coefficient

Overconformity Prevention Criteria One

의 평균절대오차값을 가짐을 알게 되었다. 반면 다중선형회귀-인공신경망 혼성모형은 1240개 전부에 대해 예측값을 계산해주며 0.992394의 결정계수값과 0.1265

의 평균절대오차값을 가져 기존 모형보다 우수함을 알게 되었다. 도 3, 4는 각 모형의 예측성능을 보여주는 패리티(parity) 도면들이며 이 도면들로부터 다중선형회귀-인공신경망 혼성모형이 기존 모형보다 우수한 성능을 가짐을 눈으로 확인할 수 있다. 한편 옥탄올-물 분배계수값의 실험평균오차는 약 0.2

이며 이 값을 중심으로 실험값과 예측값 사이의 오차를 히스토그램으로 그린 것이 도 5 및 6이다. 이 도면들은 UNIFAC을 사용한 모형은 43.51%, 다중선형회귀-인공신경망 혼성모형은 80.28%의 확률로 평균 실험오차의 범위 이내로 옥탄올-물 분배계수값을 예측하고 있음을 보여주어 다중선형회귀-인공신경망 혼성모형이 기존 모형보다 정확함을 증명해준다.
In order to show that the present invention is superior to the existing technology, the multiple linear regression-artificial neural network hybrid model thus established and the conventional group contribution model widely used, that is, the model using UNIFAC [Wienke G., Gmehling J., Prediction of octanol-water partition coefficients, Henry coefficients and water solubilities using UNIFAC , Toxicol. Environ. Chem. 1998.65.57] was compared using the data of 1240 compounds with experimental values. As a result, the model using UNIFAC calculates the prediction value only for 807, with the coefficient of determination of 0.916387 and 0.4816

It is found that the mean absolute error of. On the other hand, the multiple linear regression-artificial neural network hybrid model calculates the predicted values for all 1,240 and has a coefficient of determination of 0.992394 and 0.1265.

The average absolute error of was found to be superior to the existing model. 3 and 4 are parity diagrams showing the predictive performance of each model, and it can be seen from these figures that the multilinear regression-artificial neural network hybrid model has better performance than the conventional model. On the other hand, the experimental mean error of the octanol-water partition coefficient is about 0.2

5 and 6 show the histogram of the error between the experimental value and the predicted value. These figures show that the model using UNIFAC predicts octanol-water partition coefficients within the range of mean experimental error with a probability of 43.51% for hybrid models and 80.28% for multiple linear regression-artificial neural network hybrid models. The neural network hybrid model proves to be more accurate than the existing model.

The present invention is not limited to the above-described embodiments, and any person having ordinary skill in the art to which the present invention pertains may make various modifications without departing from the gist of the present invention as claimed in the claims. Such changes are intended to fall within the scope of the claims.

Claims (28)

A first step of inputting hydrocarbon-based experimental data among collected sample organic compounds;
A second step of preparing a molecular presenter value for the octanol-water partition coefficient of the hydrocarbon-based organic compound input in the first step;
Extracting optimal molecular descriptors;
A fourth step of separating the experimental data into a training set and a test set;
A fifth step of searching for an optimal multiple linear regression model for the training set;
A sixth step of examining the validity of the selected model;
A seventh step of repeating the fifth and sixth steps if there is no validity in the sixth step, and testing the predictive performance of the model with respect to the test set if it is valid;
An eighth step of repeating steps 4 to 7 if the performance does not satisfy the criteria in the seventh step test on the test set, and separating the three sets after standardizing the sample if the performance is satisfied;
A ninth step of searching for an optimal artificial neural network model after dividing the entire sample into three sets;
Difference between the octanol-water partition coefficient predicted value obtained by the optimal multilinear regression model satisfying the performance test in the eighth step and the octanol-water partition coefficient predicted value obtained by the optimal neural network model found in the ninth step A tenth step of comparing the absolute value of the reference value with a preset overfit prevention reference value; And
If the difference is greater than the overfit reference value, the octanol-water partition coefficient predicted by the multiple linear regression model obtained in the eighth step is adopted as the octanol-water partition coefficient value. Octanol of a hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model including the eleventh step of adopting the octanol-water partition coefficient predicted by the artificial neural network model found in step 9 as the octanol-water partition coefficient value How to get the water partition coefficient.
The octane of the hydrocarbon-based organic compound through the multiple linear regression-artificial neural network model according to claim 1, wherein the optimal molecular presenter in the third step is an independent molecular presenter whose values are not the same for all sample compounds. How to find the all-water partition coefficient.
The octanol-water distribution of a hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model according to claim 1, wherein the training set and the test set are divided in a ratio of 5: 5 to 8: 2 in the fourth step. How to find the coefficients.
2. The multiple linear regression-artificial neural network model according to claim 1, wherein in the fifth step, the multiple linear regression model searches for a multiple linear regression model by applying a genetic algorithm to the training set. Method for obtaining octanol-water partition coefficient of a hydrocarbon-based organic compound through.
The method of claim 4, wherein the genetic algorithm generates a population composed of a plurality of multiple linear regression models randomly drawn from a predetermined number of molecular representations in a pool of molecular representations. Making; Encoding each individual by combining the numbers of the extracted molecular presenters; Selecting two parent chromosomes from the created population by the Roulette Wheel method and generating offspring by a single point crossover method; Hydrocarbon-based organics through a multiple linear regression-artificial neural network model comprising the step of mutating a portion of the chromosome of the generated offspring with a certain probability and then replacing a part of the existing population with them to create a new population. A method for determining the octanol-water partition coefficient of a compound.
The octanol-water partition coefficient of a hydrocarbon-based organic compound according to claim 1, wherein the fifth step includes determining a predictive performance by a determination coefficient or an average absolute error of the regression model. How to obtain.
The method of claim 1, wherein the validity in the sixth step is to determine the octanol-water partition coefficient of the hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model.
The method of claim 1, wherein in the eighth step, if the predictive performance of the test set is similar to the predicted performance of the training set, a multiple linear regression model is determined, and the predictive performance of the test set is different from the predicted performance of the training set. A method of obtaining the standard octanol-water partition coefficients of hydrocarbon-based organic compounds using a multiple linear regression-artificial neural network model that reclassifies the training set and the test set.
The method of claim 1, wherein in the ninth step, the search range by the neural network has one hidden layer between the input layer and the output layer and is connected only in a feed forward. Method for obtaining octanol-water partition coefficient of hydrocarbon-based organic compounds by neural network model.
10. The method of claim 9, wherein a sigmoid function is used as an activation function of the hidden layer to obtain an octanol-water partition coefficient of a hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model. .
The method of claim 1, wherein the reference value for preventing overfitting is 0.3 in the tenth step, wherein the octanol-water partition coefficient of the hydrocarbon-based organic compound is obtained through a multiple linear regression-artificial neural network model.
A first step of inputting non-hydrocarbon-based experimental data among collected sample organic compounds;
Preparing a molecular presenter value for an octanol-water partition coefficient of the non-hydrocarbon-based organic compound of sample organic compounds;
Extracting optimal molecular descriptors;
A fourth step of separating the experimental data into a training set and a test set;
A fifth step of searching for an optimal multiple linear regression model for the training set;
A sixth step of examining the validity of the selected model;
A seventh step of repeating the fifth and sixth steps if there is no validity in the sixth step, and testing the predictive performance of the model with respect to the test set if it is valid;
An eighth step of repeating the fourth to seventh steps if the performance is not satisfied in the seventh step test on the test set, and separating the three sets after normalizing the sample if the performance is satisfied;
A ninth step of searching for an optimal neural network model after dividing the entire sample into three sets;
Difference between the octanol-water partition coefficient predicted value obtained by the optimal multilinear regression model satisfying the performance test in the eighth step and the octanol-water partition coefficient predicted value obtained by the optimal neural network model found in the ninth step A tenth step of comparing the absolute value of the reference value with a preset overfit prevention reference value; And
If the difference is greater than the overfit reference value, the octanol-water partition coefficient predicted by the multiple linear regression model obtained in the eighth step is adopted as the octanol-water partition coefficient value. Octane of the non-hydrocarbon-based organic compound through the multiple linear regression-artificial neural network model including the eleventh step of adopting the octanol-water partition coefficient predicted by the artificial neural network model found in step 9 as the octanol-water partition coefficient value How to find the all-water partition coefficient.
13. The method of claim 12, wherein the optimal molecular descriptors in the third step are independent molecular descriptors whose values are not the same for all the sample compounds. How to find the octanol-water partition coefficient.
13. The octanol-water of the non-hydrocarbon-based organic compound according to claim 12, wherein the training set and the test set are divided by a ratio of 5: 5 to 6: 4 in the fourth step. How to find the distribution factor.
13. The method of claim 12, wherein in the fifth step, the multiple linear regression model searches for the multiple linear regression model by applying a genetic algorithm to the training set. To obtain the octanol-water partition coefficient of non-hydrocarbon organic compounds.
The method of claim 15, wherein the genetic algorithm generates a population composed of a plurality of multiple linear regression models randomly drawn from the pool of molecular presenters. Making; Encoding each individual by combining the numbers of the extracted molecular presenters; Selecting two parent chromosomes from the created population by the Roulette Wheel method and generating offspring by a single point crossover method; A non-hydrocarbon series through a multiple linear regression-artificial neural network model comprising the step of mutating a portion of the chromosomes of the generated offspring with a certain probability and then replacing a portion of the existing population with them to create a new population. A method for determining the octanol-water partition coefficient of organic compounds.
13. The octanol-water distribution of the non-hydrocarbon-based organic compound according to claim 12, wherein the fifth step comprises determining the predictive performance by the coefficient of determination or the mean absolute error of the regression model. How to find the coefficients.
13. The method of claim 12, wherein the validity in the sixth step is to obtain the octanol-water partition coefficient of the non-hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model.
The method of claim 12, wherein in the eighth step, if the predictive performance of the test set is similar to the predicted performance of the training set, a multiple linear regression model is determined, and the predictive performance of the test set is different from the predicted performance of the training set. After that, the octanol-water partition coefficient of the non-hydrocarbon-based organic compound is obtained through a multiple linear regression-artificial neural network model that reclassifies the training set and the test set.
The method of claim 12, wherein in the ninth step, the search range by the artificial neural network has one hidden layer between the input layer and the output layer and is connected only in a feed forward. Method for obtaining octanol-water partition coefficient of non-hydrocarbon organic compounds by neural network model.
21. The method of claim 20, wherein the octanol-water partition coefficient of the non-hydrocarbon-based organic compound is calculated by using a sigmoid function as a function of activation of the hidden layer. Way.
The method of claim 12, wherein in the tenth step, the overfit prevention reference value is 1, wherein the octanol-water partition coefficient of the non-hydrocarbon-based organic compound is obtained through a multiple linear regression-artificial neural network model.
The method of claim 1, wherein the optimal molecular descriptors extracted in the third step
P ₁ : Octanol-water partition coefficient obtained by a commercial program (ClogP),
P ₂ : self-returning walk count of order 09,
P ₃ : maximum electrotopological negative variation,
P ₄ : number of CHR3 functional groups (CHR3) and
P ₅ : Radial Distribution Function-5.5 / weighted by atomic van der Waals volumes
Method for obtaining the octanol-water partition coefficient of a hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model comprising a.
The method of claim 12, wherein the optimal molecular descriptors extracted in the third step is
P ₁ : Octanol-water partition coefficient obtained by a commercial program (ClogP),
P ₂ : H autocorrelation of lag 6 / Weighted by atomic masses,
P ₃ : Minimum valency of a C atom,
P ₄ : number of Al-NO2 functional groups (Al-NO2),
P ₅ : V total size index / weighted by atomic polarizability,
P ₆ : number of nitro groups (aliphatic) and
P ₇ : number of guanidine derivatives
Method for obtaining octanol-water partition coefficient of non-hydrocarbon-based organic compounds through multiple linear regression-artificial neural network model.
In the method of calculating the octanol-water partition coefficient of a hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model,
P ₁ : Octanol-water partition coefficient obtained by a commercial program (ClogP),
P ₂ : self-returning walk count of order 09: maximum electrotopological negative variation,
P ₃ : number of CHR3 functional groups (CHR3) and
P ₄ : Radial Distribution Function-5.5 / weighted by atomic van der Waals volumes
Method for obtaining the octanol-water partition coefficient of a hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model comprising a.
In the method of obtaining the octanol-water partition coefficient of a non-hydrocarbon organic compound through a multiple linear regression-artificial neural network model,
P1: Octanol-water partition coefficient obtained by commercial program (ClogP),
P ₂ : H autocorrelation of lag 6 / Weighted by atomic masses,
P ₃ : Minimum valency of a C atom,
P ₄ : number of Al-NO ₂ functional groups (Al-NO ₂ ),
P ₅ : V total size index / weighted by atomic polarizability,
P ₆ : number of nitro groups (aliphatic) and
P ₇ : number of guanidine derivatives
Method for obtaining the octanol-water partition coefficient of the non-hydrocarbon-based organic compound through a multiple linear regression-artificial neural network model comprising a.
A computer readable and computer readable program for executing the method for obtaining the octanol-water partition coefficient of a hydrocarbon-based organic compound according to any one of claims 1 to 11, 23 and 25. Storage media.
The method for obtaining the octanol-water partition coefficient of the non-hydrocarbon-based organic compound according to any one of claims 12 to 22, 24 and 26 can be recorded by a computer program and executed by a computer. Storage media.

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