Multiscale higher-order molecular simplicial complex embedding for drug response prediction

Area of Science:

• Computational biology
• Cheminformatics
• Precision oncology

Background:

• Accurate prediction of anticancer drug response is crucial for precision oncology.
• Current computational methods struggle with molecular complexity and biological context, limiting robustness and generalization.
• There is a need for advanced frameworks that can capture intricate molecular structures and their interactions.

Purpose of the Study:

• To introduce MolDr, a novel topological deep learning framework for predicting anticancer drug response.
• To integrate molecular topology and cellular context into a unified predictive model.
• To demonstrate the superior performance and generalization capabilities of MolDr compared to existing methods.

Main Methods:

• Representing molecules as multiscale simplicial complexes to capture higher-order topological features.
• Propagating information across these complex molecular structures.
• Integrating topological molecular representations with cellular profiles for a unified prediction model.

Main Results:

• MolDr consistently outperforms or matches state-of-the-art baselines on multiple benchmarks.
• The framework achieves enhanced accuracy and robustness in continuous drug response prediction.
• MolDr demonstrates effective generalization to discrete classification tasks and highlights the value of multiscale topological features.

Conclusions:

• MolDr provides a powerful and reliable approach for predicting drug response in heterogeneous pharmacogenomic scenarios.
• Topological deep learning offers a promising direction for advancing computational drug discovery and precision medicine.
• The integration of chemical topology and biological context is key to improving predictive model performance.