Development and application of consensus in silico models for advancing high-throughput toxicological predictions.

ABSTRACT: Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also

SUBMITTER: Collins SP 

PROVIDER: S-EPMC10850302 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature