A compact review of molecular property prediction with graph neural networks
Open accessAbstract
As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks in computer-aided drug discovery workflows. The immense hype surrounding these kinds of algorithms has led to the development of many different types of promising architectures and in this review we try to structure this highly dynamic field of AI-research by collecting and classifying 80 GNNs that have been used to predict more than 20 molecular properties using 48 different datasets.
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Minimizing adverse drug events is part of the drug discovery pipeline.
“Part of the drug discovery pipeline is minimizing adverse drug events [33, 34].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [34]
Quantifying binding affinity and specificity requires labor- and cost-intensive experiments.
“However, quantifying these metrics requires labor- and cost-intensive experiments [33, 34].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [34]
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“A drug’s binding affinity and specificity to its target determine the drug’s effectiveness and potential for off-target effects 34 .”
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- Authors
- Wieder, O., Kohlbacher, S., Kuenemann, M., Garon, A., Ducrot, P., Seidel, T., & Langer, T.
- Affiliations
- University of Vienna (AT)
- Corresponding author
- Langer, Thierry
- ORCID
- Venue
- Drug Discovery Today: Technologies
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 37
- Pages
- 1-12
- ISSN
- 1740-6749 (ISSN-L)
- OpenAlex
- W3110901318
- Cited by
- 522
- References
- 58
- Citations by year
- 2026: 83 · 2025: 119 · 2024: 129 · 2023: 100 · 2022: 68 · 2021: 22 · 2019: 1
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Discovery Today Technologies (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- graph neural networks, molecular property prediction, computational drug discovery
- MeSH terms
- Neural Networks, Computer, Drug Discovery
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