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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Veröffentlicht in:	BMC bioinformatics Jg. 19; H. Suppl 19; S. 526 - 94
Hauptverfasser:	Hirohara, Maya, Saito, Yutaka, Koda, Yuki, Sato, Kengo, Sakakibara, Yasubumi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 31.12.2018 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	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 Japan Virtual screening SMILES Convolutional neural network Chemical compound TOX 21 Challenge
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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