[2603.24265] DeepDTF: Dual-Branch Transformer Fusion for Multi-Omics Anticancer Drug Response Prediction

Computer Science > Machine Learning arXiv:2603.24265 (cs) [Submitted on 25 Mar 2026] Title:DeepDTF: Dual-Branch Transformer Fusion for Multi-Omics Anticancer Drug Response Prediction Authors:Yuhan Zhao, Jacob Tennant, James Yang, Zhishan Guo, Young Whang, Ning Sui View a PDF of the paper titled DeepDTF: Dual-Branch Transformer Fusion for Multi-Omics Anticancer Drug Response Prediction, by Yuhan Zhao and 5 other authors View PDF HTML (experimental) Abstract:Cancer drug response varies widely across tumors due to multi-layer molecular heterogeneity, motivating computational decision support for precision oncology. Despite recent progress in deep CDR models, robust alignment between high-dimensional multi-omics and chemically structured drugs remains challenging due to cross-modal misalignment and limited inductive bias. We present DeepDTF, an end-to-end dual-branch Transformer fusion framework for joint log(IC50) regression and drug sensitivity classification. The cell-line branch uses modality-specific encoders for multi-omics profiles with Transformer blocks to capture long-range dependencies, while the drug branch represents compounds as molecular graphs and encodes them with a GNN-Transformer to integrate local topology with global context. Omics and drug representations are fused by a Transformer-based module that models cross-modal interactions and mitigates feature misalignment. On public pharmacogenomic benchmarks under 5-fold cold-start cell-line evaluation, DeepDTF...