《生命科学》 2026, 38(6): 1135-1151

基于人工智能与多组学策略的抗衰老及相关疾病药物发现研究进展

陈禹同^1,2,3 , 谢正伟^1,2,3,4,*

¹北京大学医学部，北京大学国际癌症研究院，北京 100191 ²北京大学医学部，北京大学-云南白药国际医学研究中心，北京 100191 ³北京大学医学部，北京大学药学院，分子与细胞药理学系，天然药物及仿生 药物国家重点实验室，北京 100191 ⁴北京亿药科技有限公司，北京 102206

摘　要：

衰老是一类机体功能随时间退化并伴随组织损伤累积的复杂生物学过程，是多种年龄相关疾病发生的重要风险因素。随着高通量组学技术的发展，基因组、转录组、蛋白质组、表观组及代谢组等多层数据为解析衰老机制提供了系统性视角，并推动多组学衰老时钟构建，实现对个体生物学年龄及衰老进程的定量化评估。同时，人工智能（artificial intelligence，AI）技术正广泛应用于抗衰老药物的发现与设计。AI驱动的药物筛选策略，如AI驱动的药物重定位、虚拟筛选以及从头分子设计，通过整合多组学数据与药物结构信息，可有效筛选适用于新适应证的已知药物，并设计结构相似及功能高效的新分子。目前，研究人员结合深度学习模型和多组学信息已成功筛选出潜在抗衰老药物，并在细胞及动物模型中验证其药理作用。这些进展表明，多组学与AI的融合不仅能够提升抗衰老药物发现效率，也为衰老机制研究和个性化干预提供新的方法与思路。

通讯作者：谢正伟 , Email:xiezhengwei@hsc.pku.edu.cn

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

CHEN Yu-Tong^1,2,3 , XIE Zheng-Wei^1,2,3,4,*

¹Peking University International Cancer Institute, Peking University Health Science Center, Beijing 100191, China ²Peking University - Yunnan Baiyao International Medical Research Center, Peking University Health Science Center, Beijing 100191, China ³State Key Laboratory of Natural and Biomimetic Drugs, Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China ⁴Beijing Gigaceuticals Tech. Co. Ltd., Beijing 102206, China

Abstract:

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 highthroughput 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, exemplified by platforms such as the Connectivity Map, to identify novel approved compounds against aging, including lifespan-extending natural products such as oridonin. Second, AI-powered virtual screening approaches that integrate molecular docking, machine learning, and graph neural networks have accelerated the identification of novel senolytics, including compounds with superior potency compared to first-generation agents. Third, generative AI models, such as variational autoencoders, generative adversarial networks, and Transformer-based architectures, have enabled the de novo design of drug-like molecules optimized for aging-related targets, as exemplified by the AI-assisted discovery of the TNIK inhibitor INS018_055. Despite these advances, several challenges remain, including differences between omics datasets, limited interpretability of deep learning models, and difficulties in translating findings from animal models to humans. Future progress will require more interpretable AI methods, personalized multi-omics aging
clocks, and greater use of human-related experimental systems and real-world clinical data. Together, the integration of AI and multi-omics technologies provides a strong foundation for precision anti-aging medicine discovery and offers new opportunities to advance translational aging research.

Communication Author：XIE Zheng-Wei , Email:xiezhengwei@hsc.pku.edu.cn