Name
Abstract | ||
|---|---|---|
The quantitative description between chemical reaction rates and nucleophilicity parameters plays a crucial role in organic chemistry. In this regard, the formula proposed by Mayr et al. and the constructed reactivity database are important representatives. However, the determination of Mayr's nucleophilicity parameter often requires time-consuming experiments with reference electrophiles in the solvent. Several machine learning (ML)-based models have been proposed to realize the data-driven prediction of in recent years. However, in addition to DFT-calculated electronic descriptors, most of them also use a set of artificially predefined structural descriptors as input, which may result in a biased representation of the nucleophile's structural information depending on descriptors' definition preference. Compared with traditional ML algorithms, graph neural networks (GNNs) can naturally take the molecule's structural information into account by applying the message passing technique. We herein proposed a SchNet-based GNN model that only takes the molecular conformation and solvent type as input. The model achieves a comparable performance to the previous benchmark study on 10-fold cross-validation of 894 data points ( = 0.91, RMSE = 2.25). To enhance the model's ability to capture the molecule's electronic information, some DFT-calculated parameters are then incorporated into the model via graph global features, and substantial improvement is achieved in the prediction precision ( = 0.95, RMSE = 1.63). These results demonstrate that both structural and electronic information are important for the prediction of , and GNN can integrate these two kinds of information more effectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1021/acs.jcim.2c00696 | Journal of Chemical Information and Modeling |
DocType | Volume | Issue |
Journal | 62 | 18 |
ISSN | Citations | PageRank |
1549-9596 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wan Nie | 1 | 0 | 0.34 |
| Deguang Liu | 2 | 0 | 0.34 |
| Shuai Cheng Li | 3 | 184 | 30.25 |
| Haizhu Yu | 4 | 0 | 0.34 |
| Yao Fu | 5 | 0 | 0.34 |