Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space.

ABSTRACT: Learning accurate drug representations is essential for tasks such as computational drug repositioning and prediction of drug side effects. 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

SUBMITTER: Yu K 

PROVIDER: S-EPMC7943198 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature