AI-integrated iPSC multi-omics: advancing phenotype discovery and drug target identification in rare diseases.

Authors

Soukaina Amniouel, Zeenat A Shyr, Ewy A Mathé, Wei Zheng

Published in

Drug discovery today. Pages 104836. Oct 16, 2026. Epub Oct 16, 2026.

Abstract

Rare diseases represent a major unmet medical need, yet the development of therapeutics for these diseases is limited by small patient cohorts, fragmented data, and uncertain disease mechanisms. Recent advances that combine patient-derived induced pluripotent stem cell (iPSC) models with multi-omics profiling and artificial intelligence offer a robust approach for phenotype definition and drug target identification that requires only minimal patient material. By integrating transcriptomic, proteomic, and metabolomic data, machine learning can produce disease-relevant molecular signatures, identify convergent pathway drivers, and prioritize druggable targets despite limited sample sizes. This short review highlights emerging in vitro models and AI-enabled workflows, discusses their strengths and limitations, and outlines practical strategies to accelerate drug discovery for rare diseases.

PMID:
42887874
Bibliographic data and abstract were imported from PubMed on 17 Oct 2026.

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