Multiscale higher-order molecular simplicial complex embedding for drug response prediction
Abstract Motivation Accurately predicting anticancer drug response is a central challenge in precision oncology. Existing computational methods, although valuable, often depend on pairwise molecular descriptors or limited graph-based encodings that cannot fully capture the complexity of molecular structures or their interactions with cellular states. These constraints hinder their robustness and generalization across diverse drugs and biological contexts, underscoring the need for more expressive frameworks. Results To address this gap, we propose MolDr, a topological deep learning framework that represents molecules as multiscale simplicial complexes and propagates information across higher-order structures. By integrating these molecular representations with cellular profiles, MolDr unifies chemical topology and biological context within a single predictive model. Comprehensive experiments show that MolDr consistently outperforms or matches state-of-the-art baselines across multiple benchmarks. It achieves stronger accuracy and robustness on continuous drug response tasks, while also generalizing effectively to discrete classification settings. Moreover, sensitivity analysis confirms the benefit of incorporating multiple topological scales, further supporting the importance of higher-order representations. Together, these results demonstrate that MolDr delivers reliable performance across heterogeneous pharmacogenomic scenarios and highlight the promise of topological modeling for advancing drug response prediction. Availability Source code freely available at https://github.com/CS-BIO/MolDr.