A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

Abstract

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritisation. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, whilst relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data is required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorised according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and a evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, whilst also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

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Cited by in corpus (11)

• Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
• Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
• Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
• Review of Natural Language Processing in Pharmacology
• A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study
• Neural Class Expression Synthesis
• Natural Language Processing for Drug Discovery Knowledge Graphs: promises and pitfalls
• The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
• Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
• Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs