Abstract
Development of new medications is a lengthy and costly process, and drug repositioning might help to shorten the development cycle. We present a machine learning (ML) workflow to drug discovery or repositioning by predicting indication for a particular disease based on drug expression profiles, with a focus on applications in psychiatry. Drugs that are not originally indicated for the disease but with high predicted probabilities serve as candidates for repurposing. This approach is widely applicable to any chemicals or drugs with expression profiles measured, even if drug targets are unknown. It is also highly flexible as virtually any supervised learning algorithms can be used. We employed the ML approach to identify repositioning opportunities for schizophrenia as well as depression and anxiety disorders. We applied various state-of-the-art ML approaches, including deep neural networks (DNNs), support vector machines (SVMs), elastic net regression, random forest, and gradient boosted trees. The predictive performance of the five approaches in cross validation did not differ substantially, with SVM slightly outperforming the others. However, other methods also reveal literature-supported repositioning candidates of different ...Continue Reading
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