A Multi-Modal Contrastive Learning Framework for Cyclic Peptide Permeability Prediction

Cyclic peptides represent a rapidly growing class of therapeutics, yet their development is often hindered by the challenge of predicting cell membrane permeability, a critical determinant of drug efficacy. Existing computational methods often struggle to integrate the diverse structural information inherent in these complex molecules, resulting in suboptimal predictive accuracy. Here, we introduce MCPerm, a multi-modal deep learning framework that synergistically integrates 1D SMILES, 2D topological, and 3D geometric information through a novel modality share and contrastive learning strategy to accurately predict cyclic peptide permeability. MCPerm fine-tunes a pretrained peptide language model for SMILES encoding and uses a parameter-sharing graph transformer for structural representation, while a dual contrastive learning mechanism enforces representational consistency both within and between modalities. On the benchmark PAMPA dataset, MCPerm achieves state-of-the-art performance, significantly outperforming leading methods. We further demonstrate its robustness and competitive transferability across three independent assays (Caco-2, MDCK, and RRCK). Our work presents a robust in silico framework that holds potential to accelerate the rational design and discovery of cell-permeable cyclic peptide drugs. Furthermore, to move beyond predictive accuracy, we introduced an attention-based visualization analysis. The results demonstrate that our model is not a "black box"; it has learned key chemical principles governing cyclic peptide permeability.