News Releases & Research Results Identification of drug candidates that suppress vascular aging by senescence scoring based on AI image recognition - High-speed, low-cost, large-scale screening for drug discovery -

News Releases & Research Results

Outline

The results of the research and development project conducted by Assistant Professor Shinsuke Yuasa of the Department of Internal Medicine (Cardiology) and Instructor Dai Kusumoto of the Center for Preventive Medicine, Keio University School of Medicine.

The key results of this R&D project are as follows:

  • A system (“Deep-SeSMo”) for evaluating cellular senescence only with micrographs of cultured vascular endothelial cells was developed using the artificial intelligence (AI), specifically, “convolutional neural network” that specializes in the image analysis. This system was applied to compound screening, resulting in the identification of drug candidates with anti-aging effects.
  • The results of this research and development project should facilitate the development of therapeutic agents for controlling senescence and epoch-making therapeutic agents for various diseases such as myocardial infarction and heart failure due to vascular aging.

This project was conducted with the support of the Research Center Network for Realization of Regenerative Medicine by AMED.

The results were published in the British scientific journal Nature Communications on January 11.

Article

Kusumoto D., et al. Anti-senescent drug screening by deep learning-based morphology senescence scoring Nature Communications
DOI: 10.1038/s41467-020-20213-0

2021.1.12

Last updated 2021.1.12