KGML-xDTD: a knowledge graph-based machine learning framework for drug treatment prediction and mechanism description.

ABSTRACT:

Background

Computational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action (MOAs) between repurposed drugs and their target diseases remain largely unknown, which is still a main obstacle for computational drug repurposing methods to be widely adopted in clinical settings.

Results

In this work, we propose KGML-xDTD: a Knowledge Graph-based Machine Learning framework for explainably predicting Drugs Treating Diseases. It is a 2-module framework that

SUBMITTER: Ma C 

PROVIDER: S-EPMC10441000 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature