Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

Sergey Sosnin, Dmitry Karlov, Igor V. Tetko, Maxim V. Fedorov

    Research output: Contribution to journalArticlepeer-review

    39 Citations (Scopus)

    Abstract

    Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different end points: it can be measured for different species using different types of administration, etc., and it is questionable if the knowledge transfer between end points is possible. We performed a comparative study of prediction multitask toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multitask learning for a large toxicity data set extracted from the Registry of Toxic Effects of Chemical Substances (RTECS). We demonstrated that multitask modeling provides significant improvement over single-output models and other machine learning methods. Our research reveals that multitask learning can be very useful to improve the quality of acute toxicity modeling and raises a discussion about the usage of multitask approaches for regulation purposes. Our MultiTox models are freely available in OCHEM platform (ochem.eu/multitox) under CC-BY-NC license. ©

    Original languageEnglish
    Pages (from-to)1062-1072
    Number of pages11
    JournalJournal of Chemical Information and Modeling
    Volume59
    Issue number3
    DOIs
    Publication statusPublished - 25 Mar 2019

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