KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug
Treatment Prediction and Mechanism Description
- URL: http://arxiv.org/abs/2212.01384v2
- Date: Tue, 25 Apr 2023 06:58:02 GMT
- Title: KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug
Treatment Prediction and Mechanism Description
- Authors: Chunyu Ma, Zhihan Zhou, Han Liu, David Koslicki
- Abstract summary: We propose KGML-xDTD: a Knowledge Graph-based Machine Learning framework for explainably predicting Drugs Treating Diseases.
We leverage knowledge-and-publication based information to extract biologically meaningful "demonstration paths" as the intermediate guidance in the Graph-based Reinforcement Learning process.
- Score: 11.64859287146094
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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
two-module framework that not only predicts the treatment probabilities between
drugs/compounds and diseases but also biologically explains them via knowledge
graph (KG) path-based, testable mechanisms of action (MOAs). We leverage
knowledge-and-publication based information to extract biologically meaningful
"demonstration paths" as the intermediate guidance in the Graph-based
Reinforcement Learning (GRL) path-finding process. Comprehensive experiments
and case study analyses show that the proposed framework can achieve
state-of-the-art performance in both predictions of drug repurposing and
recapitulation of human-curated drug MOA paths.
Conclusions: KGML-xDTD is the first model framework that can offer KG-path
explanations for drug repurposing predictions by leveraging the combination of
prediction outcomes and existing biological knowledge and publications. We
believe it can effectively reduce "black-box" concerns and increase prediction
confidence for drug repurposing based on predicted path-based explanations, and
further accelerate the process of drug discovery for emerging diseases.
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