ArticleSaudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society2026
Machine learning-guided rational engineering of ACE2-derived peptides for broad-spectrum neutralization of SARS-CoV-2 variants.
Article in Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The dynamic mutational landscape of SARS-CoV-2, especially within the spike glycoprotein's RBD, continues to undermine the effectiveness of available therapeutics. Although ACE2-derived peptides have previously been explored as spike inhibitors, their rational optimization has largely lacked systematic, data-driven selection strategies. This article describes an integrative computer-aided approach involving molecular dynamics (MD) simulations and machine learning-assisted peptide engineering to engineer potent peptide-based inhibitors targeting the RBD-ACE2 interface. Molecular dynamics simulations of wild-type and variant RBD-ACE2 complexes were first employed to identify energetically unfavorable interfacial residues, enabling the rational extraction and refinement of a 19-residue ACE2-derived peptide. A supervised regression model of the type XGBoost, trained using experimentally obtained peptide-protein affinity matrices (R
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