Machine learning for predicting lifespan-extending chemical compounds

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

The quest to slow ageing through drug discovery

Although death is inevitable, individuals have long sought to alter the course of the ageing process. Indeed, ageing has proved to be modifiable; by intervening in biological systems, such as nutrient sensing, cellular senescence, the systemic environment and the gut microbiome, phenotypes of ageing can be slowed sufficiently to mitigate age-related functional decline. These interventions can also delay the onset of many disabling, chronic diseases, including cancer, cardiovascular disease and neurodegeneration, in animal models. Here, we examine the most promising interventions to slow ageing and group them into two tiers based on the robustness of the preclinical, and some clinical, results, in which the top tier includes rapamycin, senolytics, metformin, acarbose, spermidine, NAD+ enhancers and lithium. We then focus on the potential of the interventions and the feasibility of conducting clinical trials with these agents, with the overall aim of maintaining health for longer before the end of life