Work

Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer

Year: 2025

Type: article

Abstract: Membrane permeability is a critical bottleneck in the development of cyclic peptide drugs. Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP ( https://github.com/panda1103/CPMP ), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. The model demonstrated robust predictive performance, achieving determination coefficients (R2) of 0.67 for PAMPA permeability prediction, and R2 values of 0.75, 0.62, and 0.73 for Caco-2, RRCK, and MDCK cell permeability predictions, respectively. Its performance outperforms traditional machine learning methods and graph-based neural network models. In ablation experiments, we validated the effectiveness of each component in the MAT architecture. Additionally, we analyzed the impact of data pre-training and cyclic peptide conformation optimization on model performance.

Source: Frontiers in Bioinformatics

DOI: 10.3389/fbinf.2025.1566174

Authors: Dawei Jiang, Zixi Chen, Hongli Du

Institutions: South China University of Technology, Anhui University of Science and Technology

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,077