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 ...
Source: Fight Aging! - Category: Research Authors: Tags: Daily News Source Type: blogs