Drug repurposing for Leishmaniasis with Hyperbolic Graph Neural Networks
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.11.528117v1?rss=1

Authors: Lau, Y., Gutierrez, J. M., Volkovs, M., Zuberi, S.

Abstract: Leishmanisis, a neglected tropical disease caused by protozoan parasites of the genus Leishmania, affects millions of individuals living in poverty across the world and is second to malaria in parasitic causes of death. Although current drugs for treating leishmaniasis exist, these are either highly toxic, ineffective, or expensive. For this reason, there is an urgent need to identify affordable, safer, and more effective treatments. Drug repurposing is a promising method for identifying existing molecules with the potential to treat leishmaniasis. Here, we present a deep learning model for drug repurposing based on hyperbolic graph neural networks. We leverage experimentally validated protein-drug interactions and molecular descriptors across three different parasites to train and validate our model. The final network model shows significant gains over the best baseline model, with an 11.6% increase in precision of the top scoring 0.5% protein-drug pairs. Finally, our model identified two experimental drugs that could target three L. major proteins involved in drug resistance and cell cycle regulation, which play an essential role in ensuring the parasite's survival inside the host.

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