  Development of new medications is a very lengthy and costly process. Finding
novel indications for existing drugs, or drug repositioning, can serve as a
useful strategy to shorten the development cycle. In this study, we present an
approach to drug discovery or repositioning by predicting indication for a
particular disease based on expression profiles of drugs, with a focus on
applications in psychiatry. Drugs that are not originally indicated for the
disease but with high predicted probabilities serve as good candidates for
repurposing. This framework is widely applicable to any chemicals or drugs with
expression profiles measured, even if the drug targets are unknown. It is also
highly flexible as virtually any supervised learning algorithms can be used. We
applied this approach to identify repositioning opportunities for schizophrenia
as well as depression and anxiety disorders. We applied various
state-of-the-art machine learning (ML) approaches for prediction, including
deep neural networks, support vector machines (SVM), elastic net, random forest
and gradient boosted machines. The performance of the five approaches did not
differ substantially, with SVM slightly outperformed the others. However,
methods with lower predictive accuracy can still reveal literature-supported
candidates that are of different mechanisms of actions. As a further
validation, we showed that the repositioning hits are enriched for psychiatric
medications considered in clinical trials. Notably, many top repositioning hits
are supported by previous preclinical or clinical studies. Finally, we propose
that ML approaches may provide a new avenue to explore drug mechanisms via
examining the variable importance of gene features.
