drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network

Background: For translational impact, both accurate drug response prediction and biological plausibility of predictive features are needed. We present drGT, a heterogeneous graph deep learning model over drugs, genes, and cell lines that couples prediction with mechanism-oriented interpretability via attention coefficients (ACs). Results: drGT encodes a drug–gene–cell line graph and uses ACs to surface drug–gene associations. We assess both predictive generalization (random, unseen-drug, unseen-cell, and zero-shot splits) and biological plausibility (text-mined PubMed co-mentions and comparison to a structure-based DTI predictor) on GDSC, NCI60, and CTRP datasets. Across benchmarks, drGT consistently delivers top regression performance while maintaining competitive classification accuracy. Under random 5-fold cross-validation, drGT attains an AUROC of up to 0.945 (3rd overall) and an R² up to 0.690, outperforming all baselines on regression. In leave-one-out tests for unseen cell lines and drugs, drGT achieves AUROCs of 0.706 and 0.844, and R² values of 0.692 and 0.022, and is the only model yielding positive R² for unseen drugs. In zero-shot prediction, drGT achieves an AUROC of 0.786 and a regression R² of 0.334, both representing the highest scores among all models. For interpretability, AC-derived drug–gene links recover known biology: among 976 drugs with known DTIs, 36.9% of predicted links match established DTIs, and 63.7% are supported by either PubMed abstracts or a structure-based model. Enrichment analyses of AC-prioritized genes reveal drug-perturbed biological processes, providing pathway-level explanations. Conclusions: drGT advances predictive generalization and mechanism-centered interpretability, offering state-of-the-art regression accuracy and literature-supported biological hypotheses, demonstrating how interpretable graph learning can bridge AI prediction and biological discovery. Code: https://github.com/sciluna/drGT