Convolutional neural network based on SMILES representation of compounds for detecting chemical motif
Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods a...
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| Published in | BMC bioinformatics Vol. 19; no. Suppl 19; pp. 526 - 94 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
31.12.2018
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/s12859-018-2523-5 |
Cover
| Abstract | Background
Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features.
Results
We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected.
Conclusions
The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at
http://www.dna.bio.keio.ac.jp/smiles/
, and the dataset used for performance evaluation in this work is available at the same URL. |
|---|---|
| AbstractList | Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features.BACKGROUNDPrevious studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features.We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected.RESULTSWe developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected.The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/ , and the dataset used for performance evaluation in this work is available at the same URL.CONCLUSIONSThe source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/ , and the dataset used for performance evaluation in this work is available at the same URL. Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. Results We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected. Conclusions The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/ , and the dataset used for performance evaluation in this work is available at the same URL. Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected. The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/, and the dataset used for performance evaluation in this work is available at the same URL. Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected. The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/ , and the dataset used for performance evaluation in this work is available at the same URL. Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. Results We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected. Conclusions The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at Keywords: Convolutional neural network, Chemical compound, Virtual screening, SMILES, TOX 21 Challenge Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. Results We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected. Conclusions The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/, and the dataset used for performance evaluation in this work is available at the same URL. Abstract Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. Results We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected. Conclusions The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/, and the dataset used for performance evaluation in this work is available at the same URL. |
| ArticleNumber | 526 |
| Audience | Academic |
| Author | Hirohara, Maya Sato, Kengo Koda, Yuki Saito, Yutaka Sakakibara, Yasubumi |
| Author_xml | – sequence: 1 givenname: Maya surname: Hirohara fullname: Hirohara, Maya organization: Department of Biosciences and Informatics, Keio University – sequence: 2 givenname: Yutaka surname: Saito fullname: Saito, Yutaka organization: Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST) – sequence: 3 givenname: Yuki surname: Koda fullname: Koda, Yuki organization: Department of Biosciences and Informatics, Keio University – sequence: 4 givenname: Kengo surname: Sato fullname: Sato, Kengo organization: Department of Biosciences and Informatics, Keio University – sequence: 5 givenname: Yasubumi surname: Sakakibara fullname: Sakakibara, Yasubumi email: yasu@bio.keio.ac.jp organization: Department of Biosciences and Informatics, Keio University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30598075$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3389/fenvs.2015.00085 10.1038/nmeth.3547 10.1093/bioinformatics/btw255 10.1021/ci500747n 10.1101/gr.200535.115 10.1109/TPAMI.2013.50 10.1021/jm0603365 10.1021/ci100050t 10.1007/s10822-016-9938-8 10.1021/acs.chemrestox.7b00037 10.1109/TNN.2008.2010350 10.1002/jcc.20681 10.1021/ci400187y 10.1021/ci00057a005 10.3389/fenvs.2015.00080 10.1038/nbt.3300 10.1109/TNN.2008.2005605 |
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| Keywords | Virtual screening SMILES Convolutional neural network Chemical compound TOX 21 Challenge |
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| References | F Scarselli (2523_CR7) 2009; 20 R Huang (2523_CR24) 2016; 3 PC Hawkins (2523_CR2) 2007; 50 A Micheli (2523_CR8) 2009; 20 H Zeng (2523_CR16) 2016; 32 Y Bengio (2523_CR17) 2013; 35 B Alipanahi (2523_CR12) 2015; 33 A Mayr (2523_CR6) 2016; 3 2523_CR21 2523_CR20 H Du (2523_CR25) 2017; 30 2523_CR23 2523_CR22 D Rogers (2523_CR1) 2010; 50 S Kearnes (2523_CR11) 2016; 30 J Lanchantin (2523_CR15) 2017; 22 PJ Ballester (2523_CR3) 2007; 28 2523_CR18 J Ma (2523_CR5) 2015; 55 2523_CR19 J Zhou (2523_CR13) 2015; 12 A Lusci (2523_CR9) 2013; 53 D Weininger (2523_CR4) 1988; 28 DR Kelley (2523_CR14) 2016; 26 2523_CR10 |
| References_xml | – volume: 3 start-page: 85 year: 2016 ident: 2523_CR24 publication-title: Front Environ Sci doi: 10.3389/fenvs.2015.00085 – volume: 22 start-page: 254 year: 2017 ident: 2523_CR15 publication-title: Pac Symp Biocomput – volume: 12 start-page: 931 issue: 10 year: 2015 ident: 2523_CR13 publication-title: Nat Methods doi: 10.1038/nmeth.3547 – volume: 32 start-page: 121 issue: 12 year: 2016 ident: 2523_CR16 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw255 – ident: 2523_CR22 – ident: 2523_CR20 – volume: 55 start-page: 263 issue: 2 year: 2015 ident: 2523_CR5 publication-title: J Chem Inf Model doi: 10.1021/ci500747n – ident: 2523_CR19 – volume: 26 start-page: 990 issue: 7 year: 2016 ident: 2523_CR14 publication-title: Genome Res doi: 10.1101/gr.200535.115 – volume: 35 start-page: 1798 issue: 8 year: 2013 ident: 2523_CR17 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2013.50 – volume: 50 start-page: 74 issue: 1 year: 2007 ident: 2523_CR2 publication-title: J Med Chem doi: 10.1021/jm0603365 – volume: 50 start-page: 742 issue: 5 year: 2010 ident: 2523_CR1 publication-title: J Chem Inf Model doi: 10.1021/ci100050t – ident: 2523_CR23 – ident: 2523_CR21 – volume: 30 start-page: 595 issue: 8 year: 2016 ident: 2523_CR11 publication-title: J Comput Aided Mol Des doi: 10.1007/s10822-016-9938-8 – volume: 30 start-page: 1209 issue: 5 year: 2017 ident: 2523_CR25 publication-title: Chem Res Toxicol doi: 10.1021/acs.chemrestox.7b00037 – volume: 20 start-page: 498 issue: 3 year: 2009 ident: 2523_CR8 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2010350 – ident: 2523_CR18 – volume: 28 start-page: 1711 issue: 10 year: 2007 ident: 2523_CR3 publication-title: J Comput Chem doi: 10.1002/jcc.20681 – volume: 53 start-page: 1563 issue: 7 year: 2013 ident: 2523_CR9 publication-title: J Chem Inf Model doi: 10.1021/ci400187y – volume: 28 start-page: 31 issue: 1 year: 1988 ident: 2523_CR4 publication-title: J Chem Inf Comput Sci doi: 10.1021/ci00057a005 – volume: 3 start-page: 80 year: 2016 ident: 2523_CR6 publication-title: Front Environ Sci doi: 10.3389/fenvs.2015.00080 – volume: 33 start-page: 831 issue: 8 year: 2015 ident: 2523_CR12 publication-title: Nat Biotechnol doi: 10.1038/nbt.3300 – volume: 20 start-page: 61 issue: 1 year: 2009 ident: 2523_CR7 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2005605 – ident: 2523_CR10 |
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Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning... Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have... Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning... Abstract Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep... |
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| SubjectTerms | Algorithms Analytical chemistry Artificial intelligence Artificial neural networks Binding sites Bioinformatics Biomedical and Life Sciences Chemical compound Chemical compounds Chirality Computational Biology/Bioinformatics Computer Appl. in Life Sciences Convolution Convolutional neural network Deoxyribonucleic acid DNA Drug discovery Fingerprints Functional groups Genomes Information processing Lead compounds Life Sciences Machine learning Methods Microarrays Multivariate analysis Neural networks Organic chemistry Performance evaluation Proteins Representations SMILES Software Source code TOX 21 Challenge Virtual screening |
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| Title | Convolutional neural network based on SMILES representation of compounds for detecting chemical motif |
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