Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure.

ABSTRACT: Animal testing alone cannot practically evaluate the health hazard posed by tens of thousands of environmental chemicals. Computational approaches making use of high-throughput experimental data may provide more efficient means to predict chemical toxicity. Here, we use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50

SUBMITTER: Liu J 

PROVIDER: S-EPMC6172960 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature