In Silico Peptide Design: Methods, Resources, and Role of AI.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	In Silico Peptide Design: Methods, Resources, and Role of AI.
Συγγραφείς:	Choudhury PR; Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, UP, India., Mishra SK; Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, UP, India., Yadav S; Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, UP, India., Singh S; Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, UP, India., Mathur P; Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, UP, India.
Πηγή:	Journal of peptide science : an official publication of the European Peptide Society [J Pept Sci] 2025 Dec; Vol. 31 (12), pp. e70063.
Τύπος έκδοσης:	Journal Article; Review
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: John Wiley & Sons Country of Publication: England NLM ID: 9506309 Publication Model: Print Cited Medium: Internet ISSN: 1099-1387 (Electronic) Linking ISSN: 10752617 NLM ISO Abbreviation: J Pept Sci Subsets: MEDLINE
Imprint Name(s):	Original Publication: Chichester, West Sussex, UK : John Wiley & Sons, c1995-
Ιατρικοί όροι (MeSH):	Peptides/chemistry , Peptides/chemical synthesis , Drug Design*, Molecular Dynamics Simulation ; Machine Learning ; Deep Learning ; Humans ; Computer Simulation
Περίληψη:	Peptides play essential roles in biological systems and serve as key agents in therapeutics, biomaterials, and drug delivery. Despite their broad utility, peptide design is limited by rapid degradation, low oral bioavailability, and the inefficiency of conventional synthesis and screening methods. This review provides a comprehensive overview of computational approaches that have emerged as effective alternatives, enabling the exploration of a large chemical space and the virtual screening of thousands of peptides. We detail the critical role of specialized peptide databases, computational tools, and advanced methodologies, including structure-based design, molecular dynamics (MD) simulations, and ligand-based approaches. A particular focus is placed on the transformative impact of machine learning (ML), deep learning (DL), and generative AI models, which are accelerating the discovery of novel peptides. While these methods offer promising solutions, we also address key challenges like data inconsistency, model interpretability, and the need for better forcefields. By highlighting these advancements and limitations, this review aims to provide a roadmap for leveraging computational design in peptide research. (© 2025 European Peptide Society and John Wiley & Sons Ltd.)
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Contributed Indexing:	Keywords: AlphaFold; MD simulations; computational peptide design; generative models; peptide docking; peptide prediction
Substance Nomenclature:	0 (Peptides)
Entry Date(s):	Date Created: 20251031 Date Completed: 20251031 Latest Revision: 20251031
Update Code:	20260130
DOI:	10.1002/psc.70063
PMID:	41168660
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1099-1387
DOI:	10.1002/psc.70063