Predicting serious rare adverse reactions of novel chemicals.

Motivation Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the world's 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. Results 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 compressed sensing 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. Availability The program is available at https://github.com/poleksic/side-effects. Contact [email protected] or [email protected]. Supplementary information Supplementary data are available at Bioinformatics online.