MSF-CPMP: A novel multi-source feature fusion model for prediction of cyclic peptide membrane permeability.

ABSTRACT: Cyclic peptides are becoming attractive molecules for drug discovery because of their properties with inherent stability and structural diversity. However, the high potential of cyclic peptide drugs is challenged by the limited membrane permeability cross cell membrane. To predict cyclic peptide membrane permeability (CPMP), an increased number of computational models or tools are designed and used. But these existing algorithms or models do not appropriately capture feature diversity of cyclic peptides. In this study, we introduce a novel multi-source feature fusion model called MSF-CPMP, which aims to increase the accuracy of predicted CPMP. The MSF-CPMP model incorporates three features extracted from SMILES sequences, graph-based molecular structures, and physicochemical properties of

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12703865 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature