Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology

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Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and recently adapted to the graph convolutional network (GCN), is inherently a 2D representation of 3D molecules. Herein we propose an advanced version of the GCN, called 3DGCN, which receives 3D molecular information from a molecular graph augmented by information on bond direction. While outperforming state-of-the-art deep-learning models in the prediction of chemical and biological properties, 3DGCN has the ability to both generalize and distinguish molecular rotations in 3D, beyond 2D, which has great impact on drug discovery and development, not to mention the design of chemical reactions.
Publisher
WILEY-V C H VERLAG GMBH
Issue Date
2019-09
Language
English
Article Type
Article
Citation

CHEMMEDCHEM, v.14, no.17, pp.1604 - 1609

ISSN
1860-7179
DOI
10.1002/cmdc.201900458
URI
http://hdl.handle.net/10203/270054
Appears in Collection
CH-Journal Papers(저널논문)
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