AI-integrated iPSC multi-omics: advancing phenotype discovery and drug target identification in rare diseases
- 1National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA
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.
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BibTeX
@article{amniouel2026aiintegrated,
title = {{AI-integrated iPSC multi-omics: advancing phenotype discovery and drug target identification in rare diseases}},
author = {Soukaina Amniouel and Zeenat A. Shyr and Ewy A. Mathé and Wei Zheng},
journal = {Drug discovery today},
year = {2026},
month = oct,
doi = {10.1016/j.drudis.2026.104836},
url = {https://doi.org/10.1016/j.drudis.2026.104836},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-integrated iPSC multi-omics: advancing phenotype discovery and drug target identification in rare diseases
AU - Soukaina Amniouel
AU - Zeenat A. Shyr
AU - Ewy A. Mathé
AU - Wei Zheng
PY - 2026
DA - 2026/10/16
JO - Drug discovery today
DO - 10.1016/j.drudis.2026.104836
UR - https://doi.org/10.1016/j.drudis.2026.104836
AB - 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.
SN - 1359-6446
SN - 1878-5832
ER -