Endocrine disruptor compounds (EDCs) are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. By interfering with the body’s endocrine system, these chemicals produce adverse developmental, reproductive, neurological, and immune effects in animals, abnormal growth patterns, and neurodevelopmental delays in children. Among these, certain compounds mimic the role of androgen which is responsible for controlling the development and maintenance of male sexual characteristics. In the present research, we have utilized the application of a two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling technique to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the androgen receptor binding data from the EDKB database (https://www.fda.gov/scienceresearch/endocrine-disruptor-knowledge-base/accessing-edkb-database). We have then employed the DTC-QSAR tool, available at https://dtclab.webs.com/software-tools for dataset division, feature selection, and model development. This tool is a complete package providing a user-friendly, easy-to-use GUI to develop regression or classification-based QSAR models involving variable selection techniques such as genetic algorithm and best subset selection. Dataset division was done by the euclidean distance approach method followed by feature selection using the genetic algorithm (GA) technique. The best descriptor combination selection for the pooled descriptors from the best GA-derived models was done using the tool Best Subset Selection v2.1 available at https://dtclab.webs.com/softwaretools. The final partial least squares (PLS) model was evaluated using various stringent validation criteria. The developed model is robust, predictive, and should be a useful tool to predict the binding nature of EDCs to the androgen receptor. From the model, we interpreted that hydrophobicity in terms of octanol-water partition coefficient, aliphatic -CH group count, the bulkiness of the structure, in addition to a number of non-aromatic conjugated carbon atoms (sp2 hybridized), presence of CF3 group, percentage of nitrogen present in the compounds contribute to the receptor binding affinity and thus increase toxicity, while the presence of electron-rich features like aromaticity in a molecule and presence of polar groups like alcohol, phenol or carboxyl groups decrease the receptor binding affinity and reduce toxicity. Additionally, we have also performed chemical read-across using Read-Across-v2.0 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better in the euclidean distance-based similarity considerations.
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