Name
Abstract | ||
|---|---|---|
Representation learning via deep generative models is opening a new avenue for small molecule generation in silico. Linking chemical and biological space remains a key challenge. In this paper, we debut a graph-based variational autoencoder framework to address this challenge under the umbrella of disentangled representation learning. The framework permits several inductive biases that connect the... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/BIBM52615.2021.9669692 | 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | DocType | ISBN |
Representation learning,Drugs,Biological system modeling,Conferences,Aerospace electronics,Benchmark testing,Bioinformatics | Conference | 978-1-6654-0126-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanqi Du | 1 | 0 | 1.69 |
| Yinkai Wang | 2 | 0 | 0.68 |
| Fardina Alam | 3 | 0 | 0.34 |
| Yuanjie Lu | 4 | 0 | 0.34 |
| Xiaojie Guo | 5 | 4 | 5.79 |
| Liang Zhao | 6 | 386 | 54.50 |
| Amarda Shehu | 7 | 297 | 55.09 |