Prediction of chemical biodegradability using support vector classifier optimized with differential evolution

Qi Cao, K. M. Leung

    Research output: Contribution to journalArticle


    Reliable computer models for the prediction of chemical biodegradability from molecular descriptors and fingerprints are very important for making health and environmental decisions. Coupling of the differential evolution (DE) algorithm with the support vector classifier (SVC) in order to optimize the main parameters of the classifier resulted in an improved classifier called the DE-SVC, which is introduced in this paper for use in chemical biodegradability studies. The DE-SVC was applied to predict the biodegradation of chemicals on the basis of extensive sample data sets and known structural features of molecules. Our optimization experiments showed that DE can efficiently find the proper parameters of the SVC. The resulting classifier possesses strong robustness and reliability compared with grid search, genetic algorithm, and particle swarm optimization methods. The classification experiments conducted here showed that the DE-SVC exhibits better classification performance than models previously used for such studies. It is a more effective and efficient prediction model for chemical biodegradability.

    Original languageEnglish (US)
    Pages (from-to)2515-2523
    Number of pages9
    JournalJournal of Chemical Information and Modeling
    Issue number9
    StatePublished - Sep 22 2014


    ASJC Scopus subject areas

    • Chemistry(all)
    • Chemical Engineering(all)
    • Computer Science Applications
    • Library and Information Sciences

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