Generation of Target-Selective Drug Candidate Structures Using Molecular Evolutionary Algorithmwith SVM Classifiers

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In a previous study, we reported a molecular evolutionary approach for generating chemical structures. It involved a computational experiment for reproducing a target chemical structure from a seed structure by using a fitness based on the structural similarity. In this paper, we describe a method of molecular evolutionary computation using support vector machine (SVM) classifiers for generating drug-like candidate structures with specific activity. The method is based on evolutionary operations such as crossover, mutation, and selections similar to the previous study; however, the fitness of each structure was evaluated using the SVM classifiers. We performed molecular evolutionary computation using the SVM classifiers in order to generate candidate chemical structures for antihypertensive drugs of two different therapeutic classes of angiotensin converting enzyme (ACE) and neutral endopeptidase (NEP). A computer experiment to generate the ACE-selective candidates showed that evolutionary computation could favorably increase the fitness for ACE as the alternation of generations proceeded. Another computer experiment for the NEP-selective candidates also yielded a favorable result.