Predicting serious rare adverse reactions of novel chemicals

Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the worlds economy. Due to the limitations of the animal model, computational prediction of serious, rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing. We present a novel method, based on the theory of \"compressed sensing\", which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased, and incomplete databases, but our data also demonstrates that the accuracy of our approach in predicting a serious adverse reaction (ADR) for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly. Thus, the compressed sensing based computational method reported here represents a major advance in predicting severe rare ADRs, and may facilitate reducing the time and cost of drug discovery and development.