DLFea4AMPGen de novo design of antimicrobial peptides by integrating features learned from deep learning models.

ABSTRACT: Deep learning models show promise in accelerating the design and optimization of antimicrobial peptides (AMPs), but current methods face challenges, such as low success rates, or large virtual library scales. In this study, we introduce DLFea4AMPGen, a bioactive peptide design strategy that leverages deep learning models to identify and extract key features associated with antimicrobial peptide activity. This approach enables the generation of peptide sequences with potential bioactivities. Using the SHapley Additive exPlanations (SHAP) method, we quantify the contribution of each amino acid in multifunctional peptides with potential antibacterial, antifungal, and antioxidant activities. Key feature fragments (KFFs) with the highest average contributions are extracted and classified into f

SUBMITTER: Gao H 

PROVIDER: S-EPMC12528727 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature