Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features - Research paper

Aug 3, 2026

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Growing interest focuses on pharmacological interventions for ageing and on using machine learning to mine ageing-related datasets. This study applies machine learning to DrugAge, a database of chemical compounds reported to modulate lifespan in model organisms. Four dataset variants are built to predict whether a compound extends the lifespan of Caenorhabditis elegans using biologically interpretable feature sets, including compound–protein interactions, compound–protein interactions mediated by ageing genes, and ontology/phenotype terms (Gene Ontology and WormBase physiology terms). Feature selection is used before training random forest models, and key features in the best models are biologically interpreted. Promising unlabeled candidate compounds are also proposed for further validation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

Abstract

Introduction

Motivation for anti-ageing interventions

DrugAge as a source of lifespan-modulating compounds

Machine learning for predicting lifespan extension

Dataset design and predictive modeling approach

[www.aging-us.com](www.aging-us.com) AGING 2023, Vol. 15, No. 13 Research Paper Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features Caio Ribeiro1, Christopher K. Farmer2, João Pedro de Magalhães3, Alex A. Freitas1 1School of Computing, University of Kent, Canterbury, Kent, UK 2Centre for Health Services Studies, University of Kent, Canterbury, Kent, UK 3Genomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK Correspondence to: Caio Ribeiro, Alex A. Freitas; [email:](email: C.E.Ribeiro@kent.ac.uk)[ ](email: C.E.Ribeiro@kent.ac.uk)[C.E.Ribeiro@kent.ac.uk](email: C.E.Ribeiro@kent.ac.uk), [A.A.Freitas@kent.ac.uk](A.A.Freitas@kent.ac.uk)[ ](A.A.Freitas@kent.ac.uk)[Keywords:](Keywords: lifespan-extension compounds)[ lifespan-extension compounds](Keywords: lifespan-extension compounds), longevity drugs, machine learning, feature selection Received: December 1, 2022 Accepted: June 19, 2023 Published: July 13, 2023 Copyright: © 2023 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four types of datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on: compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase’s Phenotype Ontology. To analyse these datasets, we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. In addition, we interpreted the most important features in the two best models in light of the biology of ageing. One noteworthy feature was the GO term “Glutathione metabolic process”, which plays an important role in cellular redox homeostasis and detoxification. We also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds. These include nitroprusside, which is used as an antihypertensive medication. Overall, our work opens avenues for future work in employing machine learning to predict novel life-extending compounds. INTRODUCTION Old age is a major risk factor for a number of diseases, including many types of cancer, cardiovascular and neurodegenerative diseases [1–3] . Hence, there has been growing interest in developing interventions that target the biological process of ageing, in order to extend lifespan and healthspan [4, 5] . Non-pharmacological interventions like dietary restriction and genetic interventions have been quite successful for extending the lifespan of model organisms [6–9] . However, genetic interventions are difficult to apply to humans, and arguably relatively few people would be willing to undergo dietary restriction in the long term. Hence, pharmacological interventions are currently the most promising type of anti-ageing intervention for extending human lifespan and healthspan, and this is current a very active research area in the biology of ageing [10–12] . A large number of compounds have been found by in vivo exp

FAQs

What problem does the paper address?

It aims to predict new lifespan-extending chemical compounds for C. elegans using machine learning trained on DrugAge and other biological data.

How are the prediction datasets constructed?

Four dataset types are created, each based on different biologically interpretable features describing compound–protein interactions, ageing-gene-related interactions, or ontology/phenotype terms.

What modeling and interpretation strategy is used?

The workflow applies feature selection during data preprocessing and trains random forest classifiers, then interprets the most important features in the best-performing models in terms of ageing biology.

Aug 3, 2026

Upload by Patrick