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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| Vydáno v: | Journal of chemical information and modeling Ročník 60; číslo 1; s. 22 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
27.01.2020
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| ISSN: | 1549-960X, 1549-960X |
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| 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. 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. |
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| AbstractList | 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. 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.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. |
| Author | Korolev, Vadim Mitrofanov, Artem Korotcov, Alexandru Tkachenko, Valery |
| Author_xml | – sequence: 1 givenname: Vadim orcidid: 0000-0001-6117-5662 surname: Korolev fullname: Korolev, Vadim organization: Department of Chemistry , Lomonosov Moscow State University , Leninskie gory, 1 bld. 3 , Moscow 119991 , Russia – sequence: 2 givenname: Artem orcidid: 0000-0001-8891-6862 surname: Mitrofanov fullname: Mitrofanov, Artem organization: Department of Chemistry , Lomonosov Moscow State University , Leninskie gory, 1 bld. 3 , Moscow 119991 , Russia – sequence: 3 givenname: Alexandru surname: Korotcov fullname: Korotcov, Alexandru organization: Science Data Software, LLC , 14909 Forest Landing Circle , Rockville , Maryland 20850 , United States – sequence: 4 givenname: Valery surname: Tkachenko fullname: Tkachenko, Valery organization: Science Data Software, LLC , 14909 Forest Landing Circle , Rockville , Maryland 20850 , United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31860296$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Crystallization Density Functional Theory Machine Learning Models, Molecular Neural Networks, Computer Structure-Activity Relationship |
| Title | Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability |
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