A Multi-Modal Cyclic Peptide Representation Learning Framework for Membrane Permeability Prediction.

Abstract

Cyclic peptide drugs show great potential in antiviral, antibacterial, anticancer, and immunomodulatory therapies, yet accurate prediction of their membrane permeability remains challenging. Existing approaches based on SMILES, molecular graphs, or 3D structures have inherent limitations: SMILES lack spatial information, graphs inadequately capture stereochemistry, and 3D methods are sensitive to conformational variability. Moreover, current multimodal fusion strategies often fail to effectively integrate heterogeneous molecular information. To address these challenges, we propose MultiMol, a multimodal framework that integrates SMILES sequences, molecular images, molecular graphs, and 3D conformations through tailored pre-training tasks and a scalable fusion mechanism. Experiments show that MultiMol consistently outperforms existing methods in cyclic peptide permeability prediction. Visualization and interpretability analyses further demonstrate its strong feature extraction and generalization capabilities. MultiMol also prioritizes promising KRAS-targeting cyclic peptides, supporting its practical utility in virtual screening. The code is available at https://github.com/chaoxiuxiu/multi-mol.