Format

Send to

Choose Destination
Aging (Albany NY). 2017 Jul 18;9(7):1721-1737. doi: 10.18632/aging.101264.

Machine learning for predicting lifespan-extending chemical compounds.

Author information

1
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
2
Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
3
School of Life Sciences, University of Sussex, Falmer, Brighton, UK.
4
School of Computing, University of Kent, Canterbury, UK.
#
Contributed equally

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.

KEYWORDS:

C. elegans; ageing; anti-ageing drugs; bioinformatics; longevity; machine learning; pharmaceutical interventions

PMID:
28783712
PMCID:
PMC5559171
DOI:
10.18632/aging.101264
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Impact Journals, LLC Icon for PubMed Central
Loading ...
Support Center