Predicting Endocrine Disruptors: A Deep Learning QSAR Model for Estrogen Receptor Activity
arXiv:2605.13364 · doi:10.1109/B-HTC67770.2026.11502197
Abstract
Endocrine-disrupting chemicals (EDCs) threaten human health, ecosystems, and biodiversity by interfering with hormonal signaling pathways conserved across vertebrates. Traditional in vivo assays are costly and time-consuming, limiting their capacity to screen the growing number of chemicals. To address this, we developed a deep learning-based QSAR model to predict estrogen receptor (ER) binding molecules. Using a curated dataset of 224 compounds and 2,944 molecular descriptors and fingerprints, a deep neural network (DNN) incorporating dropout and batch normalization was trained and validated. The model achieved training and test accuracies of 96.65% and 91.30%, respectively, with an ROC-AUC of 0.81, a precision of 0.82, and a recall of 0.88 for the active class. Molecular docking against estrogen receptor (PDB ID: 5TOA) confirmed that several predicted compounds exhibited binding comparable to Estradiol, sharing key interactions. This model enables rapid screening of potential EDCs, supporting efficient chemical risk assessment and contributing to biodiversity conservation by identifying compounds that may disrupt reproduction and population stability in humans and wildlife.
Copyright IEEE 2026. Permission from IEEE must be obtained for all other uses, including reprinting/republishing for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work. DOI: 10.1109/B-HTC67770.2026.11502197
References in corpus (5)
- Computational Analysis using Multi-ligand Simultaneous Docking of Withaferin A and Garcinol Reveals Enhanced BCL-2 and AKT-1 Inhibition
- Prediction of Novel CXCR7 Inhibitors Using QSAR Modeling and Validation via Molecular Docking
- In Silico Prediction and Validation of LmGt Inhibitors Using QSAR and Molecular Docking Approaches
- Multi-ligand simultaneous docking of Carica papaya leaf phytochemicals, Carpaine and Rutin, reveals multi-mechanism inhibition of cancer proteins BCL-2 and WWP1
- Deep Learning-based QSAR Model for Therapeutic Strategies Targeting SmTGR Protein's Immune Modulating Role in Host-Parasite Interaction