Name
Title | ||
|---|---|---|
Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models |
Abstract | ||
|---|---|---|
A persistent goal forde novodrug design is togenerate novel chemical compounds with desirable properties in alabor-, time-, and cost-efficient manner. Deep generative modelsprovide alternative routes to this goal. Numerous modelarchitectures and optimization strategies have been explored inrecent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aimingat three-dimensional (3D) molecule generation have also beenproposed, gaining attention for their unique advantages andpotential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developmentsin 3D molecular generative models combined with deep learningand discusses future directions forde novodrug design. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1021/acs.jcim.2c00042 | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
Keywords | DocType | Volume |
de novo drug design, deep learning, generative model, three-dimentional generation, structure-based generation, structure-based drug design | Journal | 62 |
Issue | ISSN | Citations |
10 | 1549-9596 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weixin Xie | 1 | 651 | 62.35 |
| Fanhao Wang | 2 | 0 | 0.34 |
| Yibo Li | 3 | 0 | 0.34 |
| Luhua Lai | 4 | 369 | 33.78 |
| Jianfeng Pei | 5 | 37 | 3.99 |