Androgen receptor (AR) modulation is a critical safety concern for environmental chemicals, including agrochemicals, due to its role in endocrine disruption. Existing public data sets for AR modulation are limited in size and diversity. Here, we report a large-scale high-throughput screening (HTS) campaign assessing AR binding for over 72,000 compounds from an agrochemical library using a fluorescence polarization displacement assay. Confirmatory dose-response testing identified 4,183 AR binders (5.7% hit rate) with substantial structural diversity and numerous novel scaffolds. To enable predictive modeling, we curated an unrestricted data set of 24,953 compounds with associated activity data, which we publish as part of this paper. Using this data set, we trained machine learning models based on molecular 1D and 2D descriptors and fingerprints. Gradient-boosted trees achieved the best performance, with a balanced accuracy of 0.77 and a negative predictive value of 0.98, making the model suitable for derisking large virtual libraries. External validation on existing publicly available AR data sets (CoMPARA and PubChem) demonstrated reasonable transferability (balanced accuracy 0.66 and 0.72), overcoming differences in experimental methods and composition of the compound sets. Our findings demonstrate the utility of combining HTS with machine learning for early safety assessment and provide a benchmark data set to advance AR binding prediction, complementing existing data sets.
Adamczewski et al. (Fri,) studied this question.