Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
The task of learning an expressive molecular representation is central to developing quantitative structure–activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we e...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
American Chemical Society (ACS)
2018
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| Online Access: | http://hdl.handle.net/1721.1/116837 https://orcid.org/0000-0002-8271-8723 https://orcid.org/0000-0002-2921-8201 https://orcid.org/0000-0003-2603-9694 https://orcid.org/0000-0002-2199-0379 https://orcid.org/0000-0001-7192-580X |