KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development.

ABSTRACT: Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of pred

SUBMITTER: Morger A 

PROVIDER: S-EPMC7157991 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature