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

arXiv: https://arxiv.org/abs/2102.10062 · HTML: https://mlsanity.com/paper/2102.10062/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 174 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 28 of 28)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [A Fair Comparison of Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/1912.09893/) (2019)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework](https://mlsanity.com/paper/2006.13365/) (2020)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [Open-World Knowledge Graph Completion](https://mlsanity.com/paper/1711.03438/) (2017)
- [NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding](https://mlsanity.com/paper/1812.06410/) (2018)
- [COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation](https://mlsanity.com/paper/2007.00576/) (2020)
- [OpenBioLink: A benchmarking framework for large-scale biomedical link prediction](https://mlsanity.com/paper/1912.04616/) (2019)
- [Utilising Graph Machine Learning within Drug Discovery and Development](https://mlsanity.com/paper/2012.05716/) (2020)
- [Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing](https://mlsanity.com/paper/2007.10261/) (2020)
- [COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature](https://mlsanity.com/paper/2007.12731/) (2020)
- [On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods](https://mlsanity.com/paper/2002.06914/) (2020)
- [Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing](https://mlsanity.com/paper/2007.05292/) (2020)

## Built on by (most cited first, 30 of 38)

- [KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis](https://mlsanity.com/paper/2412.16833/) (2024)
- [Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study](https://mlsanity.com/paper/2106.09700/) (2021)
- [Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery](https://mlsanity.com/paper/2105.10488/) (2021)
- [A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2203.07544/) (2022)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2112.06567/) (2021)
- [Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction](https://mlsanity.com/paper/2306.05257/) (2023)
- [Knowledge Engineering using Large Language Models](https://mlsanity.com/paper/2310.00637/) (2023)
- [Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning](https://mlsanity.com/paper/2311.15056/) (2023)
- [Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2111.10625/) (2021)
- [Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing](https://mlsanity.com/paper/2202.08712/) (2022)
- [Neural Class Expression Synthesis](https://mlsanity.com/paper/2111.08486/) (2021)
- [Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey](https://mlsanity.com/paper/2302.08261/) (2023)
- [MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction](https://mlsanity.com/paper/2406.08649/) (2024)
- [KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description](https://mlsanity.com/paper/2212.01384/) (2022)
- [Review of Natural Language Processing in Pharmacology](https://mlsanity.com/paper/2208.10228/) (2022)
- [A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets](https://mlsanity.com/paper/2105.10578/) (2021)
- [BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion](https://mlsanity.com/paper/2211.12281/) (2022)
- [Body Fat Estimation from Surface Meshes using Graph Neural Networks](https://mlsanity.com/paper/2308.02493/) (2023)
- [The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models](https://mlsanity.com/paper/2409.04103/) (2024)
- [TrialGraph: Machine Intelligence Enabled Insight from Graph Modelling of Clinical Trials](https://mlsanity.com/paper/2112.08211/) (2021)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)
- [Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices](https://mlsanity.com/paper/2305.10467/) (2023)
- [How Sharp and Bias-Robust is a Model? Dual Evaluation Perspectives on Knowledge Graph Completion](https://mlsanity.com/paper/2512.06296/) (2025)
- [Unifying biomedical knowledge in a modern multimodal graph](https://mlsanity.com/paper/2604.27269/) (2026)
- [TigerLily: Finding drug interactions in silico with the Graph](https://mlsanity.com/paper/2204.08206/) (2022)
- [OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes](https://mlsanity.com/paper/2206.02238/) (2022)
- [Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction](https://mlsanity.com/paper/2306.00041/) (2023)
- [Natural Language Processing for Drug Discovery Knowledge Graphs: promises and pitfalls](https://mlsanity.com/paper/2310.15572/) (2023)

## Abstract (arXiv)

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.