Name
Abstract | ||
|---|---|---|
Traditional drug discovery pipelines can require multiple years and billions of dollars of investment. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently reproduce the atypical patterns of quantum computers, which may improve the quality of learned tasks. We propose a suite of quantum machine learning techniques: incorporating generative adversarial networks (GAN), convolutional neural networks (CNN) and variational auto-encoders (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DAC18074.2021.9586268 | 2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC) |
DocType | ISSN | Citations |
Conference | 0738-100X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junde Li | 1 | 1 | 1.71 |
| Mahabubul Alam | 2 | 14 | 6.32 |
| Congzhou M. Sha | 3 | 0 | 0.34 |
| Jin Wang | 4 | 2 | 3.74 |
| Nikolay V Dokholyan | 5 | 141 | 18.45 |
| Swaroop Ghosh | 6 | 0 | 0.34 |