Method for predicting medicament molecule pharmacokinetic property and toxicity based on supporting vector machine

Abstract

The invention relates to a prediction method of pharmacokinetic property and toxicity of a drug molecule based on a support vector machine, which belongs to the molecule design field assisted by computers. The method fully takes advantage of the statistical learning modeling of the support vector machine, adopts an integrated method and simultaneously carries out the selection of a drug molecule descriptor and the optimization of SVM parameter. The method thereof comprises the following implementation steps: the descriptor is calculated and pre-treated, a descriptor data set is re-scaled, and the integrated method is adopted to carry out the selection of the descriptor and the optimization of the SVM parameter simultaneously. The optimization of the SVM parameter uses a conjugate gradient method to optimize penalty function C and kernel function Gamma. Genetic algorithm is used for selecting the descriptor and the individual fitness degree function adopts the fitness function which can comprehensively reflect prediction accuracy and the number of descriptors. In the integration of the selection of the descriptor and the optimization of SVM parameter, fitness degree function of each individual is calculated by SVM optimized parameter to complete the data integration of roulette, hybridization and mutation. The method fully takes two processing advantages of SVM and computer and significantly improves prediction result and efficiency.

Description

Technical field [0001] The invention relates to the field of computer-aided drug molecule design, in particular to a method for predicting pharmacokinetic properties and toxicity of drug molecules based on support vector machines. Background technique: [0002] In the early stage of drug development, the use of computers to predict the pharmacokinetic properties and toxicity of drug molecules can reduce the risk of later drug development and reduce the cost of research and development. At present, the commonly used methods for predicting the pharmacokinetic properties and toxicity of drug molecules mainly include multiple linear regression, principal component analysis, and partial least squares methods. The defects of these methods mainly include: they are only applicable to systems with small structural differences of compounds, while the actual drug molecular systems generally have large structural differences; these methods generally require a large number of samples, an...

AI Technical Summary

Problems solved by technology

However, the method using SVM is not yet able to complete the pharmacokinetics and toxicity prediction of drug molecules.

Because SVM itself still has some problems that cannot be solved by itself, such as many descriptors used in SVM modeling are repetitive and redundant, and the settings of SVM parameters including penalty function C and kernel function γ are not reasonable, etc.

The existence of these problems seriously affects the pharmacokinetic properties of drug molecules and the quality of toxicity prediction models

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