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Utilising Graph Machine Learning within Drug Discovery and Development

arXiv 2012.05716 ↗First posted 9 Dec 2020q-bio.QM cs.LGcited by 26

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Abstract (arXiv)

Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarise work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest graph machine learning will become a modelling framework of choice within biomedical machine learning.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Dec 2020.

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Citing papers per year: 2021: 12; 2022: 4; 2023: 6; 2024: 3; 2025: 1.

26 papers.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  3. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
  4. 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
  5. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  6. 2021A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspectivecited by 37
  7. 2022Graph Coloring with Physics-Inspired Graph Neural Networkscited by 29
  8. 2021Improving Inductive Link Prediction Using Hyper-Relational Factscited by 21
  9. 2021Understanding the Performance of Knowledge Graph Embeddings in Drug Discoverycited by 18
  10. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  11. 2021Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphscited by 11
  12. 2024Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networkscited by 8
14 more
  1. 2024Cluster-wise Graph Transformer with Dual-granularity Kernelized Attentioncited by 8
  2. 2023GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelismcited by 6
  3. 2023Diffusing Graph Attentioncited by 5
  4. 2023Tailoring Self-Attention for Graph via Rooted Subtreescited by 5
  5. 2021mGNN: Generalizing the Graph Neural Networks to the Multilayer Casecited by 4
  6. 2023GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discoverycited by 4
  7. 2021Machine Learning Applications for Therapeutic Tasks with Genomics Datacited by 3
  8. 2024UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Taskscited by 3
  9. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  10. 2021Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embeddingcited by 0
  11. 2022Graph Anisotropic Diffusioncited by 0
  12. 2023Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approachcited by 0
  13. 2023Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networkscited by 0
  14. 2025MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtrationcited by 0