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Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases
Advancements in healthcare, nutrition, and living conditions have significantly increased human life expectancy, and according to the World Population Prospects 2022 published by the United Nations, the global life expectancy at birth has reached 73.2 years in 2023, and is projected to increase to 77.2 years by 2050. While this represents a remarkable achievement, this demographic shift in population age is accompanied by a significant increase in prevalence of aging-related diseases, exerting substantial burden on healthcare costs, caregiver demands, and economic productivity. The most effective strategy to combat these global challenges is to increase population healthspan by promoting the early detection of age-related indications, combined with targeted interventions that prevent, delay, or treat age-related disease, ideally implemented into routine medical care (1). To achieve this goal, we need to continue to improve our understanding of the aging process, identify therapeutic aging targets to advance the development of effective antiaging therapies, and facilitate the translation of innovation from early-stage target discovery to clinical trials. This requires acting on 3 different levels, starting with identification of therapeutic targets through elaborate artificial intelligence (AI)-enabled computational methods. Next, these novel targets must undergo a panel of in vitro and in vivo validation, and a restricted number of successful targets may finally be evaluated in the clinic. The unparalleled ability of AI and machine learning (ML) systems to streamline data analysis, uncover hidden patterns in vast amounts of information, and accelerate the pace of scientific discovery, has the potential to transform aging research, revolutionizing how we view and approach aging in terms of science, society, and medicine (2). In this perspective article, we briefly outline the key milestones of aging research, highlight how advancements in deep ML systems can aid to overcome the current bottlenecks in developing effective therapies against age-related diseases, and provide an outlook on how AI is paving the path to a healthcare system focused on healthy longevity and prevention of age-related disease.