Name
Abstract | ||
|---|---|---|
Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Graph,Drug discovery,ADME,Artificial intelligence,Deep learning,Artificial neural network,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1803.06236 | 1 |
PageRank | References | Authors |
0.35 | 4 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Liu | 1 | 1 | 1.37 |
| Xiangyan Sun | 2 | 6 | 1.44 |
| Lei Jia | 3 | 1 | 1.37 |
| Jun Ma | 4 | 422 | 27.34 |
| Haoming Xing | 5 | 1 | 0.35 |
| Junqiu Wu | 6 | 1 | 0.35 |
| Hua Gao | 7 | 130 | 14.27 |
| Yax Sun | 8 | 1 | 0.35 |
| Florian Boulnois | 9 | 1 | 0.35 |
| Jie Fan | 10 | 1 | 0.69 |