Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence

Dai Kusumoto, Shinsuke Yuasa, Keiichi Fukuda

Research output: Contribution to journalReview articlepeer-review

6 Citations (Scopus)


Induced pluripotent stem cells (iPSCs) are terminally differentiated somatic cells that dif-ferentiate into various cell types. iPSCs are expected to be used for disease modeling and for devel-oping novel treatments because differentiated cells from iPSCs can recapitulate the cellular pathology of patients with genetic mutations. However, a barrier to using iPSCs for comprehensive drug screening is the difficulty of evaluating their pathophysiology. Recently, the accuracy of image analysis has dramatically improved with the development of artificial intelligence (AI) technology. In the field of cell biology, it has become possible to estimate cell types and states by examining cellular morphology obtained from simple microscopic images. AI can evaluate disease-specific phenotypes of iPS-derived cells from label-free microscopic images; thus, AI can be utilized for disease-specific drug screening using iPSCs. In addition to image analysis, various AI-based methods can be applied to drug development, including phenotype prediction by analyzing genomic data and virtual screening by analyzing structural formulas and protein–protein interactions of compounds. In the future, combining AI methods may rapidly accelerate drug discovery using iPSCs. In this review, we explain the details of AI technology and the application of AI for iPSC-based drug screening.

Original languageEnglish
Article number562
Issue number5
Publication statusPublished - 2022 May


  • artificial intelligence
  • deep learning
  • drug screening
  • image recognition
  • induced pluripotent stem cell
  • machine learning

ASJC Scopus subject areas

  • Molecular Medicine
  • Pharmaceutical Science
  • Drug Discovery


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