Comparative study of multitask toxicity modeling on a broad chemical space

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

67 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
Early online date27 Dec 2018
DOIs
Publication statusPublished - 25 Mar 2019

Keywords

  • toxicity
  • prediction
  • modeling algorithms
  • multitask learning

Fingerprint

Dive into the research topics of 'Comparative study of multitask toxicity modeling on a broad chemical space'. Together they form a unique fingerprint.

Cite this