Machine Learning to Unravel Anti-Ageing Compounds

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The ageing process is one of the greatest scientific mysteries of our time. Ageing of the population is also a major biomedical, social and economic challenge. In the past two decades, a number of drugs have been identified that can modulate ageing and preserve health in model organisms, from invertebrates like worms to mammals such as mice. In spite of this recent progress, understanding the best strategies to develop human anti-ageing interventions and preserve health in old age is still very incomplete. Besides, although a few anti-ageing drugs are being clinically tested as treatments for age-related diseases, including drugs initially associated with ageing in worms, new methods are necessary to identify and prioritize drugs that can be suitable for human applications. Indeed, identifying new longevity drugs is of widespread interest. In this project, we will develop new computational methods that take advantage of large amounts of multi-omics data available on the web, and our own compilations of the effects of hundreds of ageing-related drugs, to predict new drugs with anti-ageing properties. Specifically, methods will be developed that determine which attributes make some drugs extend animal lifespan. Furthermore, once we determine which attributes these drugs have in common, we can predict novel pro-longevity, health-promoting drugs. Worms will be employed for the experimental validation of the machine learning findings in this project because of their short lifespans and high fecundity. All methods developed will be made available to the scientific community to help guide experiments, including in other organisms. Moreover, we will develop a webserver for predicting life-extending compounds that will be made freely available online. Overall, this project will provide a significant impetus to employing predictive biology in ageing studies.