Navigating in the Chemical Space of Peptides: Computational Strategies and Molecular Features to Unveil Their Functional and Drug-like Properties

Abstract

Peptides, short chains of amino acids linked by peptide bonds, typically ranging from 2 to 50 residues, are fundamental to diverse biological processes and represent a valuable source for the development of novel bioactive compounds. In this work, we provide a comprehensive and conceptual overview of approaches to exploring the peptide chemical space. We emphasize intrinsic challenges in their chemical space investigation, particularly the complex interplay among peptide conformation, bioactivity, and bioavailability, as well as the role of sequence- and structure-derived molecular features in elucidating structure-activity relationships. Furthermore, we examine computational strategies, such as dimensionality reduction techniques, machine learning models, and similarity-based complex networks for classifying and characterizing this chemical space. Finally, we underscore the importance of interdisciplinary frameworks in advancing peptide research, highlighting how integrative approaches can uncover intersections of bioactivity across different peptide classes and leverage alternative chemical spaces to optimize and characterize peptide structures.