Molecular Property Prediction Based on a Multichannel Substructure Graph
| Autor: | Shuang Wang, Mingjian Jiang, Zhiqiang Wei, Shugang Zhang, Zhen Li, Xiaofeng Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Artificial neural network molecular property prediction Computer science business.industry Deep learning substructure-graph General Engineering Pattern recognition Molecular property Graph (abstract data type) Substructure Molecule General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 Molecular graph |
| Zdroj: | IEEE Access, Vol 8, Pp 18601-18614 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Molecular property prediction is important to drug design. With the development of artificial intelligence, deep learning methods are effective for extracting molecular features. In this paper, we propose a multichannel substructure-graph gated recurrent unit (GRU) architecture, which is a novel GRU-based neural network with attention mechanisms applied to molecular substructures to learn and predict properties. In the architecture, molecular features are extracted at the node level and molecule level for capturing fine-grained and coarse-grained information. In addition, three bidirectional GRUs are adopted to extract the features on three channels to generate the molecular representations. Different attention weights are assigned to the entities in the molecule to evaluate their contributions. Experiments are implemented to compare our model with benchmark models in molecular property prediction for both regression and classification tasks, and the results show that our model has strong robustness and generalizability. |
| Databáze: | OpenAIRE |
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