A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug–Hormone Receptor Interactions with Structure-Aware Validation

Hormones regulate many essential biological processes by interacting with specific receptors that control gene expression, metabolism, growth, and immune function. Because numerous therapeutic compounds can influence or disrupt hormone signaling pathways, understanding drug-hormone receptor interactions (DHRI) is crucial for ensuring both therapeutic efficacy and endocrine safety. However, computational approaches for predicting DHRI remain limited, and most existing models do not explicitly incorporate hormone receptor-specific information. In this study, we propose a receptor-aware deep learning framework for DHRI prediction that integrates structural drug features with contextually embedded hormone receptor information. Drug molecules are represented using a hybrid encoding strategy that combines Morgan fingerprints and graph transformer-based molecular features to capture both global chemical properties and local structural information, while hormone receptor sequences are encoded using the pretrained ESM2 protein language model to obtain biologically meaningful sequence representations. The fused drug-hormone receptor features are then processed through a multilayer neural network to predict interaction probabilities. Model performance was evaluated using three splitting strategies, including random split, cold-drug split, and scaffold-based split, to assess both predictive accuracy and generalization ability. Under the random split setting, the model achieved an accuracy of 0.93, sensitivity of 0.94, specificity and precision of 0.92, F1-score of 0.93, and MCC of 0.86 on an independent data set, while also maintaining comparable performance under the more stringent cold-drug and scaffold-based settings. Feature importance analysis showed that atomic identity, hybridization, atom degree, and bond type were key shared determinants, while receptor-stratified results revealed receptor-dependent contributions from secondary features such as aromaticity, chirality, etc. In addition, t-SNE visualization showed clear class separation after training, and molecular docking across estrogen, androgen, and glucocorticoid receptors further supported the biological relevance of the predictions. Together, these findings demonstrate that incorporating hormone receptor-specific sequence information enables more reliable and biologically meaningful prediction of drug-hormone receptor interactions.