Filtered Tensor Construction and Decomposition for Drug Repositioning

Abstract

Drug repositioning (also called "drug repurposing") is a drug development strategy that saves time and money by finding new uses for existing drugs. While a variety of computational approaches to drug repositioning exist, recent work has shown that tensor decomposition, an unsupervised learning technique for finding latent structure in multidimensional data, is a useful tool for drug repositioning. The known relationships between drugs, targets, and diseases can easily be encoded as a tensor, and by learning a low-rank representation of this tensor, decompositions can complete missing entries and therefore predict novel drug-disease associations. Multiple recent works, in the context of cancer and COVID-19 drug discovery, have used joint tensor decompositions to suggest drug repositioning candidates. While these methods make high-quality predictions, they rely on specialized decompositions formulated for specific problems. In this work, we use ENSIGN, a suite of tensor decomposition tools, to show that CP tensor decompositions of a single tensor encoding drug-target-disease associations are capable of predicting verifiable drug repositioning candidates. Because the tensors generated by drug repositioning problems are sparse, we introduce a filtered tensor construction to limit the span of the tensor without losing information needed to learn the relevant associations. We show that our method predicts verifiable novel drug-disease associations in cancer and COVID-19 data. The simplicity of our approach makes it an attractive tool for biomedical researchers looking for out-of-the-box solutions, and ENSIGN brings an added level of usability and scalability.