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Life sciences · Journal article
PLOS Computational Biology · October 21, 2026
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Motivation Predicting drug sensitivity across diverse cancer samples is a fundamental challenge in personalized therapy and drug discovery. This task requires comprehensive characterization of both drug and cancer cell line features. Current approaches often fail to fully capture the complex structural characteristics and interactions of drug compounds and struggle to integrate multi-omics features derived from the same sample, limiting their feature representation capacity. Consequently, there is a need for modeling frameworks that can jointly model molecular structural features and multi-modal cellular contexts in a unified manner. Results We present SADRP, a structurally aware multi-modal framework for drug response prediction, designed to model complex molecular structures and multi-omics data. For drug representation, SADRP incorporates Shortest Distance Path (SDP) encoding and edge encoding to capture both topological relationships and bond-type chemical semantics. For cell line profiling, a multi-view interactive encoder integrates gene expression and DNA methylation, capturing cross-omics synergistic complementarity. SADRP consistently outperforms state-of-the-art methods, while also identifying potential therapeutic candidates and biomarkers, providing mechanistic insights and supporting applications in drug discovery and personalized medicine.