Machine Learning to Unravel Anti-Ageing Compounds

Lead Research Organisation: University of Kent
Department Name: Sch of Computing

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
 
Description We have used machine learning (a type of Artificial Intelligence method) for generating computational models that predict whether or not a given drug (chemical compound) will extend the lifespan of a nematode worm (C. elegans), based on data about the effects of many drugs on a worm's lifespan (data extracted from the DrugAge database, freely available on the web). We also analysed the generated predictive models from the perspective of the biology of ageing, in order to identify the main properties of drugs (chemical compounds) that are relevant for predicting a worm's lifespan extension, and one noteworthy property was a compound's role in cellular redox homeostasis and detoxification. In addition, we used the generated models to predicted the most promising novel compounds for extending a worm's lifespan from a list of compounds with no previously known lifespan-increase effect. These identified novel compounds include e.g. nitroprusside, which is used as an antihypertensive medication.
Exploitation Route Broadly speaking, this work can lead to future work in employing machine learning to predict novel life-extending compounds.
Sectors Pharmaceuticals and Medical Biotechnology