Lysenko, Artem and Sharma, Alokanand and Boroevich, Keith and Tsunoda, Tatsuhiko (2018) An integrative machine learning approach for prediction of toxicity - related drug safety. Life Science Alliance, 1 (6). pp. 1-14. ISSN 2575-1077
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Abstract
Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Alokanand Sharma |
| Date Deposited: | 20 Feb 2019 00:34 |
| Last Modified: | 20 Feb 2019 00:34 |
| URI: | https://repository.usp.ac.fj/id/eprint/11321 |
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