Name
Title | ||
|---|---|---|
DeepAtomicCharge: a new graph convolutional network-based architecture for accurate prediction of atomic charges |
Abstract | ||
|---|---|---|
Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction methods have been proposed to speed up the calculation of high-accuracy atomic charges in recent years. However, most of them used a set of predefined molecular properties, such as molecular fingerprints, for model construction, which is knowledge-dependent and may lead to biased predictions due to the representation preference of different molecular properties used for training. To solve the problem, we present a new architecture based on graph convolutional network (GCN) and develop a high-accuracy atomic charge prediction model named DeepAtomicCharge. The new GCN architecture is designed with only the atomic properties and the connection information between the atoms in molecules and can dynamically learn and convert molecules into appropriate atomic features without any prior knowledge of the molecules. Using the designed GCN architecture, substantial improvement is achieved for the prediction accuracy of atomic charges. The average root-mean-square error (RMSE) of DeepAtomicCharge is 0.0121 e, which is obviously more accurate than that (0.0180 e) reported by the previous benchmark study on the same two external test sets. Moreover, the new GCN architecture needs much lower storage space compared with other methods, and the predicted DDEC atomic charges can be efficiently used in large-scale structure-based drug design, thus opening a new avenue for high-performance atomic charge prediction and application. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/bib/bbaa183 | BRIEFINGS IN BIOINFORMATICS |
Keywords | DocType | Volume |
atomic charge, deep learning, graph convolutional network, structure-based virtual screening | Journal | 22 |
Issue | ISSN | Citations |
3 | 1467-5463 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jike Wang | 1 | 0 | 0.68 |
| Dongsheng Cao | 2 | 1 | 6.44 |
| Cunchen Tang | 3 | 0 | 1.01 |
| Lei Xu | 4 | 0 | 1.69 |
| Qiaojun He | 5 | 0 | 0.68 |
| Bo Yang | 6 | 0 | 0.34 |
| Xi Chen | 7 | 0 | 2.03 |
| Huiyong Sun | 8 | 0 | 1.01 |
| Tingjun Hou | 9 | 427 | 54.50 |