Convolutional embedding of attributed molecular graphs for physical property prediction

Coley, Connor W.; Barzilay, Regina; Green, William H.; Jaakkola, Tommi S.; Jensen, Klavs F.

doi:10.1021/acs.jcim.6b00601

Cited by 470Computational Drug Discovery MethodsGraph theory and applicationsMachine Learning in Materials Science

Convolutional embedding of attributed molecular graphs for physical property prediction

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Coley, Connor W., Barzilay, Regina, Green, William H., Jaakkola, Tommi S., Jensen, Klavs F.·2017·Journal of Chemical Information and Modeling

DOI 10.1021/acs.jcim.6b00601

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Abstract

The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii. By working directly with the full molecular graph, there is a greater opportunity for models to identify important features relevant to a prediction task. Unlike other graph-based approaches, our atom featurization preserves molecule-level spatial information that significantly enhances model performance. Our models learn to identify important features of atom clusters for the prediction of aqueous solubility, octanol solubility, melting point, and toxicity. Extensions and limitations of this strategy are discussed.

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Molecular properties including octanol, solubility melting point, and biological activity can be evaluated using neural fingerprints as demonstrated by Coley et al. and others, and used to predict new features of drug molecules.
“In this way, molecular properties including octanol, solubility melting point, and biological activity can be evaluated as demonstrated by Coley et al. and others and be used to predict new features of the drug molecules [18].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [18]
CNNs have provided more accurate predictions than descriptor-based DNNs in some cases.
“In some cases, CNN has provided more accurate predictions than descriptor based DNNs.71”
From QSAR without borders (2020) · cites this paper as [71]

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Authors: Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F.
Affiliations: Massachusetts Institute of Technology (US)
MIT Computer Science and Artificial Intelligence Laboratory (US)
Corresponding author: Jensen, Klavs F.
ORCID: Coley, Connor W.
Barzilay, Regina
Green, William H.
Jaakkola, Tommi S.
Jensen, Klavs F.
Venue: Journal of Chemical Information and Modeling
Publisher: American Chemical Society (ACS)
Published: 2017-07-25
Volume: 57
Issue: 8
Pages: 1757-1772
ISSN: 1549-9596 (ISSN-L)
1549-960X
DOI: 10.1021/acs.jcim.6b00601
OpenAlex: W2735246657
Cited by: 470
References: 55
Citations by year: 2026: 27 · 2025: 44 · 2024: 66 · 2023: 65 · 2022: 57 · 2021: 83 · 2020: 61 · 2019: 41 · 2018: 19 · 2017: 6
FWCI: 26.06
Type: journal article
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc
Locations: Journal of Chemical Information and Modeling (publishedVersion)
PubMed (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders: Defense Advanced Research Projects Agency (W911NF-16-2-0023)
Division of Graduate Education (1122374)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords: convolutional neural networks, attributed graphs, molecular representation, aqueous solubility prediction, melting point prediction, toxicity prediction, QSPR, QSAR
MeSH terms: Octanols, Computer Graphics, Solubility, Water, Neural Networks, Computer, Toxicity Tests, Transition Temperature, Informatics, Physical Phenomena

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Convolutional embedding of attributed molecular graphs for physical property prediction

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