Computational Platforms for Membrane-Active Peptide Therapeutic Design

Abstract

Peptide-based therapeutics are gaining popularity as next-generation drugs because of their potent bioactivity, high specificity, and broad applications in immunomodulatory, antiviral, anticancer, and antimicrobial therapy. Membrane-active peptides (MAPs) are particularly promising, yet poor solubility, stability, pharmacokinetics, manufacturing complexity, and limited delivery options have restricted their clinical translation. This review focuses on computational approaches developed to address these challenges at the peptide-design stage and bridge the gap between preclinical promise and clinical utility. The goal here is to provide a broad overview of tools for in silico MAP design, prediction of functional structural ensembles and physicochemical properties, structure–function relationships, drug-target interactions, and formulation strategies for in vivo delivery. These approaches span bioinformatics, molecular dynamics and molecular modeling, and emerging artificial intelligence and machine learning platforms, including hybrid computational-experimental strategies, chemical modification and conjugation, and nanotechnology-based delivery systems. These tools are improving key peptide–drug properties, including plasma stability, target specificity, efficacy, and delivery. Continued advances in computational design may enable MAPs to address challenging diseases, engage traditionally undruggable targets, and translate their substantial therapeutic potential into clinically effective treatments.