Transforming Drug Discovery for Aging Research with AI-Based Integrated Approaches

Abstract

Aging is a complex biological process which is a consequence of an unbalance between cellular dynamical equilibriums, environmental constraints and, from a larger perspective, evolutionary constraints. The key characteristic of aging is that it propagates through the body to progressively affect all organ systems. Because of this systemic nature, the study of aging-related diseases and the design of appropriate drugs and treatments have been shown to be challenging. Thanks to recent technological developments, it is now possible to make a more efficient and systematic use of the large amount of biological and patient data which has been generated and accumulated over the years. All these different data types provide insights into the mechanisms of aging from different and complementary perspectives and designing methods to extract as much information as possible from them is of paramount importance. Advanced multimodal deep learning methods are transforming aging research and drug discovery. AI based platforms can be deployed to identify promising targets for aging-related diseases and AI can be used for the generation of novel drug-like compounds with good activity profiles and suitable chemical properties. Those new computational methods have the potential to revolutionize drug discovery in oncology, a field currently facing an increasing pressure to deliver new drugs in a faster and more efficient ways.