A graph-convolutional neural network model for the prediction of chemical reactivity
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Abstract
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
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Given the potential of ML for predicting chemical reactions, ML may also be able to help with the discovery of new materials for electrocatalysts and/or proton conductors to facilitate ammonia production.
“Given the potential of ML for predicting chemical reactions [140], ML may also be able to help with the discovery of new materials for electrocatalysts and/or proton conductors to facilitate ammonia production.”
From Tackling climate change with machine learning (2023) · cites this paper as [140]
Researchers are experimenting with supervised learning and thermal imaging systems to rapidly identify promising catalysts and chemical reactions.
“Researchers are also experimenting with supervised learning and thermal imaging systems to rapidly identify promising catalysts and chemical reactions [140,686], as described in Section 2.1.1.”
From Tackling climate change with machine learning (2023) · cites this paper as [140]
Graph representation learning methods such as GNNs have been developed to learn robust and meaningful molecular representations despite the incomplete interactome and to inductively infer new edges between node pairs.
“To this end, graph representation learning methods, such as GNNs, have been developed to learn robust and meaningful representations of molecules despite the incomplete interactome [102] and to inductively infer new edges between pairs of nodes [154].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [154]
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- Authors
- Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F.
- Affiliations
- Massachusetts Institute of Technology (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Barzilay, Regina
- Jensen, Klavs F.
- ORCID
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2019
- Volume
- 10
- Issue
- 2
- Pages
- 370-377
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC6335848
- OpenAlex
- W2903262661
- Cited by
- 679
- References
- 34
- Citations by year
- 2026: 44 · 2025: 91 · 2024: 82 · 2023: 89 · 2022: 107 · 2021: 110 · 2020: 115 · 2019: 39 · 2018: 1 · 2017: 1
- FWCI
- 49.55
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Chemical Science (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-nd)
- Funders
- Defense Advanced Research Projects Agency (ARO W911NF-16-2-0023)
- National Science Foundation (1122374)
- 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 › Mathematics › Geometry and Topology › Graph theory and applications
- Keywords
- graph convolutional networks, reactivity prediction, molecular property prediction
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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.
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