Drug repurposing using link prediction on knowledge graphs with applications to non-volatile memory
Date
2021-12-02
DOI
Authors
Chin, Sang
Wood, Andrew
Cohen, Sarel
Taraz, Martin
Kibig, Otto
Waddington, Daniel
Friedrich, Tobias
Version
Accepted manuscript
OA Version
Citation
S. Chin, A. Wood, S. Cohen, M. Hershcovitch, M. Taraz, O. Kibig, D. Waddington, T. Friedrich. 2021. "Drug Repurposing Using Link Prediction on Knowledge Graphs with Applications to Non-volatile Memory." 10th International Conference on Complex Networks and their Applications (2021)
Abstract
The active global SARS-CoV-2 pandemic caused more than
167 million cases and 3.4 million deaths worldwide. The development
of completely new drugs for such a novel disease is a challenging, time
intensive process and despite researchers around the world working on this
task, no e ective treatments have been developed yet. This emphasizes
the importance of drug repurposing, where treatments are found among
existing drugs that are meant for different diseases. A common approach
to this is based on knowledge graphs, that condense relationships between
entities like drugs, diseases and genes. Graph neural networks (GNNs) can
then be used for the task at hand by predicting links in such knowledge
graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently
developed the Dr-COVID model. We further extend their work using
additional output interpretation strategies. The best aggregation strategy
derives a top-100 ranking of candidate drugs, 32 of which currently being
in COVID-19-related clinical trials. Moreover, we present an alternative
application for the model, the generation of additional candidates based
on a given pre-selection of drug candidates using collaborative filtering.
In addition, we improved the implementation of the Dr-COVID model
by significantly shortening the inference and pre-processing time by
exploiting data-parallelism. As drug repurposing is a task that requires
high computation and memory resources, we further accelerate the post-processing
phase using a new emerging hardware | we propose a new
approach to leverage the use of high-capacity Non-Volatile Memory for
aggregate drug ranking.