Predicting Mildly Age-Slowing Drugs will be a Focus of Future Research
It is clear that new ways of analyzing large amounts of data via machine learning will be used extensively in the near future in the field of aging research, employed to speed up the process of finding new drug targets and small molecules that might alter metabolism to slightly slow aging. This will no doubt be a sizable component of the longevity industry, if we judge the near future by the present distribution of companies and efforts. I can't say that I think that is likely to produce sizable benefits in aging humans, however, when compared to the rational design of therapies to specifically repair underlying causes of aging.
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.
Seems like the machine-learning/small molecule approach could just as easily and effectively be applied to other therapies though right? Hopefully the money will flow toward things with the best results