Journal of Chemical Information and Modeling, volume 60, issue 1, pages 22-28
Graph Convolutional Neural Networks as "general-Purpose" Property Predictors: The Universality and Limits of Applicability
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Science Data Software, LLC, 14909 Forest Landing Circle, Rockville, Maryland 20850, United States
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Publication type: Journal Article
Publication date: 2019-12-20
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 5.6
ISSN: 15499596, 1549960X
General Chemistry
Computer Science Applications
General Chemical Engineering
Library and Information Sciences
Abstract
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. Classical approach of rigorous feature engineering in ML typically improves the performance of the predictive model, but at the same time, it narrows down the scope of applicability and decreases the physical interpretability of predicted results. In this study, we present graph convolutional neural networks (GCNN) as an architecture that allows to successfully predict the properties of compounds from diverse domains of chemical space, using a minimal set of meaningful descriptors. The applicability of GCNN models has been demonstrated by a wide range of chemical domain-specific properties. Their performance is comparable to state-of-the-art techniques; however, this architecture exempts from the need to carry out precise feature engineering.
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Citations by publishers
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5
10
15
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25
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1 publication, 1.72%
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Hindawi Limited
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1 publication, 1.72%
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Annual Reviews
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1 publication, 1.72%
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5
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25
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Korolev V. et al. Graph Convolutional Neural Networks as "general-Purpose" Property Predictors: The Universality and Limits of Applicability // Journal of Chemical Information and Modeling. 2019. Vol. 60. No. 1. pp. 22-28.
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Korolev V., Mitrofanov A., Korotcov A., Tkachenko V. Graph Convolutional Neural Networks as "general-Purpose" Property Predictors: The Universality and Limits of Applicability // Journal of Chemical Information and Modeling. 2019. Vol. 60. No. 1. pp. 22-28.
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TY - JOUR
DO - 10.1021/acs.jcim.9b00587
UR - https://doi.org/10.1021%2Facs.jcim.9b00587
TI - Graph Convolutional Neural Networks as "general-Purpose" Property Predictors: The Universality and Limits of Applicability
T2 - Journal of Chemical Information and Modeling
AU - Korotcov, Alexandru
AU - Tkachenko, Valery
AU - Korolev, Vadim
AU - Mitrofanov, Artem
PY - 2019
DA - 2019/12/20 00:00:00
PB - American Chemical Society (ACS)
SP - 22-28
IS - 1
VL - 60
SN - 1549-9596
SN - 1549-960X
ER -
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@article{2019_Korolev,
author = {Alexandru Korotcov and Valery Tkachenko and Vadim Korolev and Artem Mitrofanov},
title = {Graph Convolutional Neural Networks as "general-Purpose" Property Predictors: The Universality and Limits of Applicability},
journal = {Journal of Chemical Information and Modeling},
year = {2019},
volume = {60},
publisher = {American Chemical Society (ACS)},
month = {dec},
url = {https://doi.org/10.1021%2Facs.jcim.9b00587},
number = {1},
pages = {22--28},
doi = {10.1021/acs.jcim.9b00587}
}
Cite this
MLA
Copy
Korolev, Vadim, et al. “Graph Convolutional Neural Networks as "general-Purpose" Property Predictors: The Universality and Limits of Applicability.” Journal of Chemical Information and Modeling, vol. 60, no. 1, Dec. 2019, pp. 22-28. https://doi.org/10.1021%2Facs.jcim.9b00587.
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