Modular Flows: Differential Molecular Generation
Authors: Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our models can be cast as message passing temporal networks, and result in superlative performance on the tasks of density estimation and molecular generation. In particular, our generated samples achieve state of the art on both the standard QM9 and ZINC250K benchmarks. |
| Researcher Affiliation | Collaboration | Yogesh Verma, Samuel Kaski, Markus Heinonen Aalto University {yogesh.verma, samuel.kaski, markus.heinonen}@aalto.fi Vikas Garg Yai Yai Ltd and Aalto University vgarg@csail.mit.edu; vikas@yaiyai.fi |
| Pseudocode | Yes | Algorithm 1 Training Mod Flow |
| Open Source Code | No | The paper does not provide an explicit statement or a link to a public repository for the source code of the described methodology. |
| Open Datasets | Yes | Data. We trained and evaluated all the models on ZINC250k (Irwin et al., 2012) and QM9 (Ramakrishnan et al., 2014) datasets. |
| Dataset Splits | No | The paper mentions training on ZINC250k and QM9 datasets but does not explicitly provide the percentages or counts for training, validation, and test splits, nor does it refer to specific predefined splits. |
| Hardware Specification | No | The paper states 'The calculations were performed using resources within the Aalto University Science-IT project.' but does not specify any particular hardware components like GPU or CPU models, or memory details. |
| Software Dependencies | No | The paper states 'The models were implemented in Py Torch (Paszke et al., 2019)' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We trained with the Adam optimizer (Kingma and Ba, 2014) for 50-100 epochs (until the training loss became stable), with batch size 1000 and learning rate 0.001. |