Advances in anti-aging and age-related disease drug discovery based on artificial intelligence and multi-omics

作者：Yu-Tong CHEN, Zheng-Wei XIE · 发表于：Shengming kexue · 年份：2026 · DOI：10.3724/cbls.2026096 · 研究领域：Cell Image Analysis Techniques、Computational Drug Discovery Methods、Genetics, Aging, and Longevity in Model Organisms

Aging is a highly complex biological process driven by the progressive accumulation of cellular damage, ultimately leading to functional decline and increased susceptibility to chronic diseases. As aging increases the risk of most chronic diseases, it involves changes across many biological systems that may not be adequately treated with the traditional “one disease-one target-one drug” strategy. These conventional approaches are not only time-consuming and costly but also difficult to capture the complex, interconnected biological changes that occur during aging. Recent advances in high-throughput multi-omics technologies, including genomics, transcriptomics, proteomics, epigenomics, and metabolomics, have enabled the study of aging from a systems perspective. By integrating these large-scale datasets, researchers have gained more profound insights into aging-related molecular pathways and regulatory networks. This progress has also enabled the development of aging clocks, such as Horvath, PhenoAge, and GrimAge, which use molecular features to estimate biological age and assess the effects of anti-aging interventions. In parallel, artificial intelligence (AI) has emerged as a transformative force in anti-aging drug discovery by enabling efficient integration, interpretation, and prediction across complex biological datasets. This review summarizes three major AI-driven strategies. First, AI-enabled drug repositioning leverages transcriptomic perturbation signatures, exemplif...