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We present\n                      <jats:italic>drGT<\/jats:italic>\n                      , a heterogeneous graph deep learning model over drugs, genes, and cell lines that couples prediction with mechanism-oriented interpretability via attention coefficients (ACs).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We assess both\n                      <jats:italic>predictive generalization<\/jats:italic>\n                      (random, unseen-drug, unseen-cell, and zero-shot splits) and\n                      <jats:italic>biological plausibility<\/jats:italic>\n                      (use of text-mined PubMed gene-drug co-mentions and comparison to a structure-based DTI predictor) on GDSC, NCI60, and CTRP datasets. Across benchmarks,\n                      <jats:italic>drGT<\/jats:italic>\n                      consistently delivers top regression performance while maintaining competitive classification accuracy for drug sensitivity. Under random 5-fold cross-validation,\n                      <jats:italic>drGT<\/jats:italic>\n                      attains an AUROC of up to 0.945 (3rd overall) and an\n                      <jats:inline-formula>\n                        <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      up to 0.690, outperforming all baselines on regression. In leave-one-out tests for unseen cell lines and drugs,\n                      <jats:italic>drGT<\/jats:italic>\n                      achieves AUROCs of 0.706 and 0.844, and\n                      <jats:inline-formula>\n                        <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      values of 0.692 and 0.022, the only model yielding positive\n                      <jats:inline-formula>\n                        <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      for unseen drugs. In zero-shot prediction,\n                      <jats:italic>drGT<\/jats:italic>\n                      achieves an AUROC of 0.786 and a regression\n                      <jats:inline-formula>\n                        <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      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 predictive model. Enrichment analyses of AC-prioritized genes reveal drug-perturbed biological processes, providing pathway-level explanations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      drGT advances predictive generalization and mechanism-centered interpretability, offering state-of-the-art regression accuracy and literature-supported biological hypotheses that demonstrate the use of graph learning from heterogeneous input data for biological discovery. Code:\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/sciluna\/drGT\" ext-link-type=\"uri\">https:\/\/github.com\/sciluna\/drGT<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-026-06417-z","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:44:49Z","timestamp":1781603089000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph"],"prefix":"10.1186","volume":"27","author":[{"given":"Yoshitaka","family":"Inoue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hunmin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianfan","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Kuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Augustin","family":"Luna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"6417_CR1","unstructured":"Chang Y-T, Hoffman EP, Yu G, Herrington DM, Clarke R, Wu C-T, Chen L, Wang Y. 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