Hyperbolic Molecular Representation Learning for Drug Repositioning

• URL: http://arxiv.org/abs/2208.06361v1
• Date: Wed, 6 Jul 2022 20:20:29 GMT
• Title: Hyperbolic Molecular Representation Learning for Drug Repositioning
• Authors: Ke Yu, Shyam Visweswaran, Kayhan Batmanghelich
• Abstract summary: A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure. Here, we develop a semi-supervised drug embedding that incorporates two sources of information. We show that the learned drug embedding can induce the hierarchical relations among drugs.
• Score: 19.73556079390888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Our qualitative results support that the learned drug embedding can induce the hierarchical relations among drugs. We demonstrate that the learned drug embedding can be used for drug repositioning.

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