Year: 2026
Type: article
Abstract: Drug response prediction (DRP) requires models that jointly characterize molecular drug structure and heterogeneous cellular states. Although recent deep learning approaches have integrated multi-omics profiles with drug representations, many treat omics views as parallel feature sources or merge them before relational modeling, which can weaken omics-view-specific cell-line relation structures. Here, we propose MulDR, a structured multi-view relational learning framework that preserves omics-view-specific cell-line relation structures before drug-conditioned cell-drug interaction. MulDR constructs separate relation structures for pathway activity, gene expression, mutation, and DNA methylation, learns view-specific relational cell representations, and uses complementary motif-level, geometry-aware molecular graph, and Morgan fingerprint drug representations to condition their fusion. With this design, cell-line similarity remains view-dependent until the interaction stage, rather than being prematurely compressed into a single feature or relation space. Extensive experiments show that MulDR consistently outperforms representative and recent baselines under the same evaluation protocol, reducing root mean square error by 6.07% relative to the strongest baseline. Cold-start evaluations on unseen drugs and cell lines further show that MulDR generalizes to previously unseen entities, while ablation studies show that removing either view-specific relational modeling or drug-conditioned fusion consistently compromises predictive performance. Together, these results suggest that preserving omics-specific cell-line relations before drug-conditioned interaction modeling provides an effective strategy for robust DRP.
Source: Briefings in Bioinformatics
DOI: 10.1093/bib/bbag619
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,885
OpenAlex ID: W7225458115