Work

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

Year: 2026

Type: review

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.

Source: Drug Discovery Today

DOI: 10.1016/j.drudis.2026.104836

Authors: Soukaina Amniouel, Zeenat A. Shyr, Ewy A. Mathé, Wei Zheng

Institutions: National Institutes of Health, National Center for Advancing Translational Sciences

Language: English

Topic: Pluripotent Stem Cells Research

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,720