Name
Abstract | ||
|---|---|---|
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of performing de-novo molecular design. Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1811.09766 | 2 | 0.37 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rim Assouel | 1 | 2 | 0.37 |
| Mohamed H. Ahmed | 2 | 1935 | 119.39 |
| Marwin H. S. Segler | 3 | 73 | 7.31 |
| Amir Saffari | 4 | 3 | 1.73 |
| Yoshua Bengio | 5 | 42677 | 3039.83 |