REPRODUCIBLE DRUG REPURPOSING: WHEN SIMILARITY DOES NOT SUFFICE.

Repurposing existing drugs for new uses has attracted considerable attention over the past years. To identify potential candidates that could be repositioned for a new indication, many studies make use of chemical, target, and side effect similarity between drugs to train classifiers. Despite promising prediction accuracies of these supervised computational models, their use in practice, such as for rare diseases, is hindered by the assumption that there are already known and similar drugs for a given condition of interest. In this study, using publicly available data sets, we question the prediction accuracies of supervised approaches based on drug similarity when the drugs in the training and the test set are completely disjoint. We first build a Python platform to generate reproducible similarity-based drug repurposing models. Next, we show that, while a simple chemical, target, and side effect similarity based machine learning method can achieve good performance on the benchmark data set, the prediction performance drops sharply when the drugs in the folds of the cross validation are not overlapping and the similarity information within the training and test sets are used independently. These intriguing results suggest revisiting the assumptions underlying the validation scenarios of similarity-based methods and underline the need for unsupervised approaches to identify novel drug uses inside the unexplored pharmacological space. We make the digital notebook containing the Python code to replicate our analysis that involves the drug repurposing platform based on machine learning models and the proposed disjoint cross fold generation method freely available at github.com/emreg00/repurpose.