Predicting the frequencies of drug side effects

Diego Galeano, Shantao Li, Mark Gerstein, Alberto Paccanaro

Abstract:

A central issue in drug risk-benefit assessment is identifying frequencies of side effects in
humans. Currently, frequencies are experimentally determined in randomised controlled
clinical trials. We present a machine learning framework for computationally predicting
frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures
of drugs and side effects that are both reproducible and biologically interpretable. We show
the usefulness of our approach on 759 structurally and therapeutically diverse drugs and
994 side effects from all human physiological systems. Our approach can be applied to any
drug for which a small number of side effect frequencies have been identified, in order to
predict the frequencies of further, yet unidentified, side effects. We show that our model is
informative of the biology underlying drug activity: individual components of the drug signatures
are related to the distinct anatomical categories of the drugs and to the specific drug
routes of administration.