From clock to clock: Therapeutic target discovery for aging and age-related diseases

Jianjiu Chen1, Geoffrey Ho Duen Leung1, Howell Leung1

  • 1Insilico Medicine Hong Kong Ltd, Unit 310, 3/F, Building 8W, Hong Kong Science and Technology Park, Hong Kong, China.

Ageing Research Reviews
|August 14, 2025
PubMed

Insights

Aging clocks, powered by machine learning, are emerging as powerful tools for identifying new drug targets to combat age-related diseases and improve healthspan. This approach aids in developing novel therapeutics for a growing global elderly population.

Area of Science:

  • Gerontology and Bioinformatics
  • Computational Biology and Drug Discovery

Background:

  • The global aging population requires new therapies for age-related diseases.
  • Identifying therapeutic targets that address both aging and disease is crucial.

Purpose of the Study:

  • To review dual-purpose target identification strategies for aging and age-related diseases.
  • To highlight the role of aging clocks in identifying therapeutic targets.

Main Methods:

  • Assessment of the hallmarks of aging for target identification.
  • Utilizing machine learning (ML) and artificial intelligence (AI) models for aging clock development.
  • Identifying clock-associated genes to validate known drug targets.

Main Results:

  • Aging clocks, initially biomarkers, show significant potential for therapeutic target identification.
  • A substantial number of known drug targets were reestablished by identifying clock-associated genes.
  • Aging clocks can be applied to population stratification and disease/treatment monitoring.

Conclusions:

  • Aging clock research, fueled by multi-omics data and AI, is advancing rapidly.
  • This progress promises innovative treatments for the healthcare needs of an aging global population.
  • Dual-purpose target identification strategies are key to developing effective anti-aging therapeutics.

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