Abstract 3139: drGT: Attention-guided gene assessment of drug response utilizing a drug-cell-gene heterogeneous network

Abstract Background: Accurate drug response prediction alone is insufficient for translational impact; computational models must also generate biologically plausible hypotheses. We present drGT, a heterogeneous graph neural network (GNN) 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 describe drug-gene associations. We assess both predictive generalization (random, unseen-drug, unseen-cell, and zero-shot splits) and biological credibility (text-mined PubMed co-mentions and comparison to a structure-based DTI predictor) on GDSC1, GDSC2, NCI60, and CTRP datasets. Here, we evaluate both regression (IC50 values) and binary classification (cell-line sensitivity) settings to reflect typical pharmacogenomic experiments; we report R2 for regression and AUROC for classification tasks. Across benchmarks, drGT consistently delivers top regression performance while maintaining competitive classification accuracy. Under random 5-fold cross-validation, where 20% of samples are randomly masked for testing in each fold, drGT attains an R2 of up to 0.690 (1st overall against similar methods) and an AUROC of up to 0.945 (3rd overall). In leave-one-out generalization tests, where either all samples from one cell line or all samples of one drug are excluded during training, drGT achieves R2 values of 0.692 (1st) and 0.022 (1st) and AUROCs of 0.706 (3rd) and 0.844 (2nd), respectively. In the zero-shot prediction test, trained on GDSC1 and evaluated on GDSC2, drGT achieves an R2 of 0.334 (1st) and an AUROC of 0.786 (1st), both of which represent the highest scores among all models. For interpretability, AC-derived drug-gene links recover known biology: among 976 drugs from the NCI60 dataset with known DTIs, 36.9% of predicted links match established DTIs, and 63.7% are supported by either PubMed abstracts or a state-of-the-art (SOTA) structure-based DTI prediction model. Moreover, an over-representation analysis of AC-ranked genes identifies mechanism of action (MoA)-consistent pathways, such as KRAS signaling, for relevant kinase inhibitors, providing pathway-level explanations for the model predictions. Conclusions: drGT advances predictive generalization and mechanism-centered interpretability with AC-derived drug-gene links, offering SOTA 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 Citation Format: Yoshitaka Inoue, Hunmin Lee, Tianfan Fu, Rui Kuang, Augustin Luna. drGT: Attention-guided gene assessment of drug response utilizing a drug-cell-gene heterogeneous network [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3139.