Multi-task learning framework for drug–target interactions and adverse effects prediction

Abstract

Drug-target interactions (DTIs) and adverse drug reactions (ADRs) are biological processes that interact closely but are difficult to model together, preventing the investigation of off-target effects and system perturbations that underlie drug safety. We introduce a single deep-learning system to predict both DTI and ADR using diverse molecular and protein representations of drug SMILES sequences and three-dimensional molecular graphs as well as protein sequences and structural features provided by AlphaFold. Curated DTI and drug-ADR data have a common RxNorm identifier that facilitates the cross-task correspondence between these two data.The proposed context-aware multi-task model uses variational auto-encoder bottleneck to both regularize shared latent space and multihead predictors for binary DTI classification and multi-label ADR with strong label imbalance. In contrast to the previous methods where interaction and safety modeling are decoupled or a single-modality evidence is used, in the model, drug-protein-ADR context is learned jointly. At the drug and protein concentrations, cold-start split analysis show decision-relevant predictions, which are calibrated, and good extrapolation to unfamiliar objects. In addition to predictive performance, the pipeline has a modular and reproducible prediction framework between featurization and inference enabling scalable experimentation. It continues the integration of DTI-ADR modeling as a conceptual method of early safety triage, risk-conscious virtual screening and translational drug discovery. Under stringent protein-level cold-start evaluation, the proposed framework achieves strong and stable performance, attaining a DTI AUROC of 91.60%, AUPRC of 85.46%, and F1-score of 78.39%, alongside ADR prediction with a weighted AUROC of 98.80% and weighted AUPRC of 94.54%, consistently reproduced across multiple random seeds.