Random forest classification for predicting lifespan-extending chemical compounds

Abstract Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learnin...

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Autores principales:	Sofia Kapsiani, Brendan J. Howlin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4cb24d92d0de41d5869a47b418347c1d
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spelling	oai:doaj.org-article:4cb24d92d0de41d5869a47b418347c1d2021-12-02T15:22:57ZRandom forest classification for predicting lifespan-extending chemical compounds10.1038/s41598-021-93070-62045-2322https://doaj.org/article/4cb24d92d0de41d5869a47b418347c1d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93070-6https://doaj.org/toc/2045-2322Abstract Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.Sofia KapsianiBrendan J. HowlinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sofia Kapsiani Brendan J. Howlin Random forest classification for predicting lifespan-extending chemical compounds
description	Abstract Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.
format	article
author	Sofia Kapsiani Brendan J. Howlin
author_facet	Sofia Kapsiani Brendan J. Howlin
author_sort	Sofia Kapsiani
title	Random forest classification for predicting lifespan-extending chemical compounds
title_short	Random forest classification for predicting lifespan-extending chemical compounds
title_full	Random forest classification for predicting lifespan-extending chemical compounds
title_fullStr	Random forest classification for predicting lifespan-extending chemical compounds
title_full_unstemmed	Random forest classification for predicting lifespan-extending chemical compounds
title_sort	random forest classification for predicting lifespan-extending chemical compounds
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4cb24d92d0de41d5869a47b418347c1d
work_keys_str_mv	AT sofiakapsiani randomforestclassificationforpredictinglifespanextendingchemicalcompounds AT brendanjhowlin randomforestclassificationforpredictinglifespanextendingchemicalcompounds
_version_	1718387381501952000

Random forest classification for predicting lifespan-extending chemical compounds

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