Drug repurposing for Leishmaniasis with Hyperbolic Graph Neural Networks

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 parasites survival inside the host. Author summaryLeishmanisis is a deadly and neglected tropical disease. Current treatments are ineffective, highly toxic, and unaffordable for the majority of affected individuals who live in extreme poverty. To accelerate the discovery and development of novel treatments against this disease, we develop a model that proposes existing drugs with the potential to treat leishmaniasis. Our methodology is based on hyperbolic graph neural networks, a class of deep learning models that can incorporate protein-drug interaction information from similar but well-studied parasites, in addition to utilizing chemical and molecular features. Empirically, this additional information leads to improved predictions of drug interactions with L. major proteins. Overall, the protein-drug pairs predicted by our model suggests two existing drugs that could target essential pathways for growth and survival of Leishmania parasites, which could be exploited either as a booster to increase the therapeutic effect of an existing anti-leishmaniasis drug, or as a novel chemotherapeutic treatment against the disease.