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Machine learning for predicting lifespan-extending chemical compounds

Barardo, Diogo G., Newby, Danielle, Thornton, Daniel, Ghafourian, Taravat, Pedro de Magalhães, João, Freitas, Alex A. (2017) Machine learning for predicting lifespan-extending chemical compounds. Aging, 9 (7). pp. 1721-1737. ISSN 1945-4589. (doi:10.18632/aging.101264)

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https://doi.org/10.18632/aging.101264

Abstract

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans’ lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound’s chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans’ lifespan in the DGIdb database, where the effect of the compounds on an organism’s lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti-inflammatories, and compounds for gonadotropin-releasing hormone therapies.

Item Type: Article
DOI/Identification number: 10.18632/aging.101264
Divisions: Faculties > Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 25 Jul 2017 14:58 UTC
Last Modified: 29 May 2019 19:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62389 (The current URI for this page, for reference purposes)
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