Name
Abstract | ||
|---|---|---|
Biogenic compounds are important materials for drug discovery and chemical biology. In this work, we report a quasi-biogenic molecule generator (QBMG) to compose virtual quasi-biogenic compound libraries by means of gated recurrent unit recurrent neural networks. The library includes stereo-chemical properties, which are crucial features of natural products. QMBG can reproduce the property distribution of the underlying training set, while being able to generate realistic, novel molecules outside of the training set. Furthermore, these compounds are associated with known bioactivities. A focused compound library based on a given chemotype/scaffold can also be generated by this approach combining transfer learning technology. This approach can be used to generate virtual compound libraries for pharmaceutical lead identification and optimization.Open image in new window |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s13321-019-0328-9 | Journal of Cheminformatics |
Keywords | Field | DocType |
Deep learning, Recurrent neural networks, Natural product, Virtual library | Training set,Data mining,Drug discovery,Scaffold,Computer science,Molecule,Transfer of learning,Chemical biology,Recurrent neural network,Artificial intelligence,Deep learning,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 1 | 1758-2946 |
Citations | PageRank | References |
2 | 0.38 | 18 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuangjia Zheng | 1 | 17 | 6.37 |
| Xin Yan | 2 | 12 | 2.93 |
| Qiong Gu | 3 | 38 | 7.97 |
| Yuedong Yang | 4 | 196 | 23.47 |
| Yunfei Du | 5 | 72 | 14.62 |
| Yutong Lu | 6 | 307 | 53.61 |
| Jun Xu | 7 | 9 | 1.53 |