Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability

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. The classical approach of rigorous feature engineering in ML typically im...

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Veröffentlicht in:Journal of chemical information and modeling Jg. 60; H. 1; S. 22
Hauptverfasser: Korolev, Vadim, Mitrofanov, Artem, Korotcov, Alexandru, Tkachenko, Valery
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 27.01.2020
Schlagworte:
Algorithms
Crystallization
Density Functional Theory
Machine Learning
Models, Molecular
Neural Networks, Computer
Structure-Activity Relationship
ISSN:1549-960X, 1549-960X
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Zusammenfassung: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. The 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 (GCNNs) as an architecture that allows for successfully predicting 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1549-960X
1549-960X
DOI:10.1021/acs.jcim.9b00587