Junction Tree Variational Autoencoder for Molecular Graph Generation
Authors: Wengong Jin, Regina Barzilay, Tommi Jaakkola
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin. |
| Researcher Affiliation | Academia | 1MIT Computer Science & Artiļ¬cial Intelligence Lab. Correspondence to: Wengong Jin <wengong@csail.mit.edu>. |
| Pseudocode | Yes | Algorithm 1 Tree decoding at sampling time |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We use the ZINC molecule dataset from Kusner et al. (2017) for our experiments, with the same training/testing split. It contains about 250K drug molecules extracted from the ZINC database (Sterling & Irwin, 2015). |
| Dataset Splits | No | The paper mentions using 'the same training/testing split' as a cited work, but does not explicitly specify the percentages or counts for training, validation, or test sets within the paper itself for the main VAE model. A 10-fold cross-validation is mentioned for a secondary Sparse Gaussian Process, but not for the primary VAE, and no distinct validation set is described for the VAE training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions tools and components like RDKit and GRU, but does not provide specific version numbers for any software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | Yes | To be comparable with the above baselines, we set the latent space dimension as 56, i.e., the tree and graph representation h T and h G have 28 dimensions each. |