Specifically, the random forest algorithm was employed to predict whether a compound will increase the lifespan of C. (2017) to further explore the use of the DrugAge database for predicting compounds with anti-ageing properties 4. This study builds on the work conducted by Barardo et al. The best performing model, with an AUC score of 0.80, was applied to predict the class of the compounds in the DGIdb database. Feature selection was performed using random forest’s feature importance measure. The features used to build the model were molecular descriptors and gene ontology terms. elegans based on the data of the DrugAge database 1, 4. (2017) built a random forest model to predict whether a compound would increase the lifespan of C. The findings showed that population-specific ageing clocks were more accurate in predicting chronological age and quantifying biological age than generic ageing clocks 10.īarardo et al. (2018) developed a deep learning-based haematological ageing clock using blood samples from Canadian, South Korean, and Eastern European populations, with millions of subjects 10. The study identified the top five most critical blood markers for determining chronological age in humans, which were albumin, glucose, alkaline phosphatase, urea and erythrocytes 9. (2016) developed a deep learning neural network that predicted human chronological age from a basic blood test 9. The network showed that resistance to oxidative stress and lifespan extension clustered in a few pharmacological classes, most of them related to intercellular signalling 8. (2014) developed a pharmacological network to identify pharmacological classes related to the ageing of C. (2009) reported that genetic deletion of S6 protein kinase 1, a component of the mTOR signalling pathway, increased the lifespan of mice and protected against age-related conditions 7. (2009) found that treating mice with rapamycin, an inhibitor of the mTOR pathway, extended the median and maxim lifespan of the mice 6. (2006) presented the first evidence that long-term dietary deprivation can improve longevity in a multicellular species, Caenorhabditis elegans ( C. Several ageing studies have identified interventions that extend the lifespan of model organisms ranging from nematodes and fruit flies to rodents, using dietary restrictions, genetic modifications and pharmaceutical interventions. Interventions targeting the cellular and molecular process of ageing have the potential to delay and protect against age-related conditions.
Ageing is a predominant risk factor for many conditions including various types of cancers, cardiovascular and neurodegenerative diseases 3, 4. Pharmacological interventions for longevity extensionĪgeing is a major health, social and financial challenge, characterised by the deterioration of the physiological processes of an organism 1, 2. 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. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules.
The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The features of the model were ranked using the Gini importance measure of the random forest algorithm. 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. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. 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. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases.
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