TY - JOUR
T1 - Convolutional neural network based on SMILES representation of compounds for detecting chemical motif
AU - Hirohara, Maya
AU - Saito, Yutaka
AU - Koda, Yuki
AU - Sato, Kengo
AU - Sakakibara, Yasubumi
N1 - Funding Information:
This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas “Frontier Research on Chemical Communications”. This work was also funded by a MEXT-supported Program for the Strategic Research Foundation at Private Universities. Publication costs are funded by Grant-in-Aid for Scientic Research on Innovative Areas “Frontier Research on Chemical Communications”.
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/12/31
Y1 - 2018/12/31
N2 - 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.
AB - 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.
KW - Chemical compound
KW - Convolutional neural network
KW - SMILES
KW - TOX 21 Challenge
KW - Virtual screening
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U2 - 10.1186/s12859-018-2523-5
DO - 10.1186/s12859-018-2523-5
M3 - Article
C2 - 30598075
AN - SCOPUS:85059287866
SN - 1471-2105
VL - 19
JO - BMC Bioinformatics
JF - BMC Bioinformatics
M1 - 526
ER -