Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
Chinese Academy of Sciences · ShanghaiTech University · +2 more institutions
Abstract
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the rising amounts of data, but the gap between what these neural networks learn and what human beings can comprehend is growing. Moreover, this gap may induce distrust and restrict deep learning applications in practice. Here, we introduce a new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery data sets. We demonstrate that Attentive FP achieves…
Citation impact
- FWCI
- 49.93
- Percentile
- 100%
- References
- 52
Authors
11- ZXZhaoping Xiong
Chinese Academy of Sciences, ShanghaiTech University, Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences
- DWDingyan Wang
Chinese Academy of Sciences, Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences
- XLXiaohong Liu
Chinese Academy of Sciences, ShanghaiTech University, Shanghai Institute of Materia Medica
- FZFeisheng Zhong
Chinese Academy of Sciences, Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences
- XWXiaozhe Wan
Chinese Academy of Sciences, Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences
Topics & keywords
- Artificial intelligence
- Mechanism (biology)
- Distrust
- Computer science
- Drug discovery
- Deep learning
- Variety (cybernetics)
- Representation (politics)