Molecular graph convolutions: moving beyond fingerprints
Open accessAbstract
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.
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Graph representation learning methods have successfully integrated patient records with molecular, genomic, and disease networks to personalize patient predictions.
“Graph representation learning methods have been successful in integrating patient records with molecular, genomic, and disease networks to personalize patient predictions.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [104]
Spatial-based models are more flexible to handle multi-source graph inputs such as edge inputs, directed graphs, signed graphs, and heterogeneous graphs, because these graph inputs can be incorporated into the aggregation function easily.
“Spatial-based models are more flexible to handle multi-source graph inputs such as edge inputs [15], [27], [86], [95], [96], directed graphs [25], [72], signed graphs [97], and heterogeneous graphs [98], [99], because these graph inputs can be incorporated into the aggregation function easily.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [86]
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- [9]Duvenaud DK Maclaurin D Iparraguirre J Bombarell R Hirzel T Aspuru-Guzik A Adams RP Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems 2015 2224 2232
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- Authors
- Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P.
- Affiliations
- Stanford University (US)
- Google (United States) (US)
- Corresponding author
- Kearnes, Steven
- Venue
- Journal of Computer-Aided Molecular Design
- Published
- 2016-08
- Volume
- 30
- Issue
- 8
- Pages
- 595-608
- ISSN
- 0920-654X (ISSN-L)
- 1573-4951
- PMC
- PMC5028207
- OpenAlex
- W2290847742
- Cited by
- 1,631
- References
- 37
- Citations by year
- 2026: 106 · 2025: 166 · 2024: 185 · 2023: 206 · 2022: 198 · 2021: 247 · 2020: 224 · 2019: 165 · 2018: 104 · 2017: 27 · 2016: 2
- FWCI
- 91.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Computer-Aided Molecular Design (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health (1S10RR02664701, 5U19AI109662-02)
- 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
- molecular fingerprints, graph neural networks, small molecules, ligand-based virtual screening, drug discovery, cheminformatics
- MeSH terms
- Machine Learning, Computer Graphics, Pharmaceutical Preparations, Ligands, Drug Design, Molecular Structure, Neural Networks, Computer, Computer-Aided Design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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