RT Journal Article
SR Electronic
T1 An integrative machine learning approach for prediction of toxicity-related drug safety
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e201800098
DO 10.26508/lsa.201800098
VO 1
IS 6
A1 Artem Lysenko
A1 Alok Sharma
A1 Keith A Boroevich
A1 Tatsuhiko Tsunoda
YR 2018
UL https://www.life-science-alliance.org/content/1/6/e201800098.abstract
AB 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.