Machine learning-based stability optimization design and virtual screening of the anti-pulmonary fibrosis peptide DR3penA.

Authors

Junpeng Ran, Jiao Zhang, Xiang Zhang, Weili Yang, Xinyu Cai, Wei Dai, Junqiu Xie

Published in

Journal of pharmaceutical sciences. Pages 104564. Oct 26, 2026. Epub Oct 26, 2026.

Abstract

The novel anti-fibrotic peptide DR3penA, previously developed by our research group, demonstrates substantial therapeutic potential; however, its inherent limited metabolic stability in vivo, attributable to its linear structure, hinders clinical translation. Although cyclization represents a key strategy for enhancing peptide structural rigidity, exhaustive trial-and-error exploration across the vast chemical modification space entails prohibitive computational and experimental costs. In this study, we constructed a machine learning-assisted virtual screening framework for the rational optimization of peptide stability and bioactivity. This framework integrates a stability prediction model with an ensemble bioactivity prediction model based on XGBoost and LightGBM, and employs comprehensive scoring alongside Pareto front analysis to perform multi-objective screening of candidate analogs. Computational evaluation showed that, under leakage-audited cluster splitting, the stability XGBoost model exhibits strong ranking capability (test Spearman ρ = 0.751; NDCG@10 = 0.969), and the XGBoost-LightGBM ensemble achieves high predictive performance for a pan-peptide bioactivity proxy (test R² = 0.822). A virtual library of 95 analogues was generated by predefined chemical rules; cyclization sites were enumerated rather than discovered de novo, and the models were used to rank these schemes. Three analogues (DR3penA_001, _011, and _076) were nominated as a representative panel covering distinct cyclization chemistries, rather than the three highest composite scores. Altogether, this hierarchical, leakage-aware framework enables stability-prioritized, multi-objective nomination of DR3penA analogues and provides a transferable, data-driven strategy for peptide redesign.

PMID:
42933566
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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