Applications of in silico methods to analyze the toxicity and estrogen receptor-mediated properties of plant-derived phytochemicals.

A myriad of phytochemicals may have potential to lead toxicity and endocrine disruption effects by interfering with nuclear hormone receptors. In this examination, the toxicity and estrogen receptor-binding abilities of a set of 2826 phytochemicals were evaluated. The endpoints mutagenicity, carcinogenicity (both CAESAR and ISS models), developmental toxicity, skin sensitization and estrogen receptor relative binding affinity (ER_RBA) were studied using the VEGA QSAR modeling package. Alongside the predictions, models were providing possible information for applicability domains and most similar compounds as similarity sets from their training sets. This information was subjected to perform the clustering and classification of chemicals using Self-Organizing Maps. The identified clusters and their respective indicators were considered as potential hotspot structures for the specified data set analysis. Molecular screening interpretations of models were exhibited accurate predictions. Moreover, the indication sets were defined significant clusters and cluster indicators with probable prediction labels (precision). Accordingly, developed QSAR models showed good predictive abilities and robustness, which observed from applicability domains, representation spaces, clustering and classification schemes. Furthermore, the designed new path could be useful as a valuable approach to determine toxicity levels of phytochemicals and other environmental pollutants and protect the human health.