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Sosnin, S.* ; Karlov, D.* ; Tetko, I.V. ; Fedorov, M.V.*

Comparative study of multitask toxicity modeling on a broad chemical space.

J. Chem. Inf. Model. 59, 1062-1072 (2019)
Verlagsversion Postprint Forschungsdaten DOI
Open Access Green
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Neural-networks; Web Server; Qsar; Prediction; Classification; Descriptors
ISSN (print) / ISBN 0021-9576
e-ISSN 1520-5142
Quellenangaben Band: 59, Heft: 3, Seiten: 1062-1072 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Verlagsort 1155 16th St, Nw, Washington, Dc 20036 Usa
Begutachtungsstatus Peer reviewed