Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong, Hong Kong SAR, China.
Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, United Arab Emirates.
J Gerontol A Biol Sci Med Sci. 2024 Sep 1;79(9). doi: 10.1093/gerona/glae006.
Given the unprecedented rate of global aging, advancing aging research and drug discovery to support healthy and productive longevity is a pressing socioeconomic need. Holistic models of human and population aging that account for biomedical background, environmental context, and lifestyle choices are fundamental to address these needs, but integration of diverse data sources and large data sets into comprehensive models is challenging using traditional approaches. Recent advances in artificial intelligence and machine learning, and specifically multimodal transformer-based neural networks, have enabled the development of highly capable systems that can generalize across multiple data types. As such, multimodal transformers can generate systemic models of aging that can predict health status and disease risks, identify drivers, or breaks of physiological aging, and aid in target discovery against age-related disease. The unprecedented capacity of transformers to extract and integrate information from large and diverse data modalities, combined with the ever-increasing availability of biological and medical data, has the potential to revolutionize healthcare, promoting healthy longevity and mitigating the societal and economic impacts of global aging.
鉴于全球人口老龄化速度前所未有，推进老龄化研究和药物发现以支持健康和富有成效的长寿是一项紧迫的社会经济需求。综合考虑人类和人口老龄化的生物医学背景、环境背景和生活方式选择的模型对于满足这些需求至关重要，但使用传统方法将不同的数据源和大数据集整合到综合模型中具有挑战性。人工智能和机器学习的最新进展，特别是基于多模态转换器的神经网络，使得开发能够跨多种数据类型进行泛化的高能力系统成为可能。因此，多模态转换器可以生成能够预测健康状况和疾病风险、识别生理衰老的驱动因素或突破的衰老系统模型，并有助于针对与年龄相关的疾病发现靶点。转换器从大量和多样化的数据模式中提取和整合信息的空前能力，加上生物和医疗数据的可用性不断增加，有可能彻底改变医疗保健，促进健康长寿，并减轻全球老龄化的社会和经济影响。
