Advances in anti-aging Drug research leveraging multi-omics and artificial intelligence
TL;DR
This review systematically elaborates on the pathological characteristics and molecular mechanisms of aging, analyzes the application paradigms of multi-omics in biomarker screening, mechanism elucidation, and high-throughput screening, and discusses the core value and technical bottlenecks of AI in target prediction, virtual screening, and trial design.
Abstract
The intensifying global population aging has rendered the development of anti-aging drugs a core challenge in the life sciences domain. Traditional models struggle to address the systemic and networked nature of aging. Multi-omics technologies provide a panoramic perspective for deciphering molecular networks of aging, while Artificial Intelligence (AI) has demonstrated significant advantages in target discovery, drug screening, and clinical trial optimization. This review systematically elaborates on the pathological characteristics and molecular mechanisms of aging, analyzes the application paradigms of multi-omics in biomarker screening, mechanism elucidation, and high-throughput screening, and discusses the core value and technical bottlenecks of AI in target prediction, virtual screening, and trial design. Simultaneously, this paper addresses critical challenges including ethical controversies and data standardization currently confronting the field, and explores the prospects of precision anti-aging drug development and personalized treatment strategies driven by deep integration of multi-omics and AI. This approach offers novel theoretical frameworks and practical pathways for extending human healthspan.