Discovery of mitochondrial modulators using target-aggregated machine learning

Lizhuo Wang; Peng Yang; Sirui Wu; Fanding Xu; Wu Su; Le Shi; Hui Guo; Cui Li; Jiankang Liu; Zhiwei Yang; JianGang LONG

doi:10.1016/j.jpha.2026.101579

Discovery of mitochondrial modulators using target-aggregated machine learning

Journal of Pharmaceutical Analysis 2026 Open access

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Authors: Lizhuo Wang, Peng Yang, Sirui Wu, Fanding Xu, Wu Su, Le Shi, Hui Guo, Cui Li, Jiankang Liu, Zhiwei Yang, JianGang LONG

DOI: 10.1016/j.jpha.2026.101579

Cited by: 0

References: 5

Abstract

• A target-agnostic, data-integrated machine learning strategy was developed to overcome data scarcity and enable robust predictive modeling. • Multiple machine learning and deep learning algorithms were systematically evaluated, with XGBoost achieving a top ROC AUC of 0.943. • Virtual screening of a large chemical library identified six novel mitochondrial modulators, all experimentally validated in human cell assays. • A scalable, target-independent framework was established for phenotypic drug discovery in complex biological systems.

Fields of Study & Subjects

Computational Drug Discovery Methods Machine Learning in Materials Science Synthesis and Characterization of Heterocyclic Compounds

Publication details

Journal / source: Journal of Pharmaceutical Analysis
Volume: 16
Issue: 7
Pages: 101579–101579
Publication date: 9 February 2026
Language: EN

References 5