POI-3DGCN: Predicting odor intensity of monomer flavors based on three-dimensionally embedded graph convolutional network

作者：

Highlights：

• 3DGCN-based default prediction model is proposed.

• The contribution of atomic features to predicting the OI of monomer flavors in 3DGCN.

• Global pooling based on iterative content-based attention improve model performance.

• Application of monomer flavor data shows that our model outperforms existing model.

摘要

•3DGCN-based default prediction model is proposed.•The contribution of atomic features to predicting the OI of monomer flavors in 3DGCN.•Global pooling based on iterative content-based attention improve model performance.•Application of monomer flavor data shows that our model outperforms existing model.

论文关键词：Odor intensity,Odor thresholds,Deep learning,Molecular descriptors,Graph convolutional network

论文评审过程：Received 25 October 2021, Revised 25 February 2022, Accepted 26 March 2022, Available online 4 April 2022, Version of Record 12 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116997