Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

ABSTRACT: We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three

SUBMITTER: Trisciuzzi D 

PROVIDER: S-EPMC6691737 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature