Torsional Diffusion for Molecular Conformer Generation
Authors: Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate torsional diffusion by comparing the generated and ground-truth conformers in terms of ensemble RMSD (Section 4.3) and properties (Section 4.4). |
| Researcher Affiliation | Academia | 1CSAIL, Massachusetts Institute of Technology 2Dept. of Physics, Harvard University |
| Pseudocode | Yes | Algorithm 1: Energy-based training epoch |
| Open Source Code | Yes | Code is available at https://github.com/gcorso/torsional-diffusion. |
| Open Datasets | Yes | Dataset We evaluate on the GEOM dataset [Axelrod and Gómez-Bombarelli, 2022], which provides gold-standard conformer ensembles generated with metadynamics in CREST [Pracht et al., 2020]. and We used the code and data released by Geo Mol and Geo Diff released under MIT license and the GEOM datasets released under CC0 1.0 license. |
| Dataset Splits | Yes | We use the train/val/test splits from Ganea et al. [2021] and use the same metrics to compare the generated and ground truth conformer ensembles: |
| Hardware Specification | No | Approximately 2000 GPU-hours on an internal cluster. |
| Software Dependencies | No | The paper mentions several software tools and packages used (e.g., RDKit, CREST, GFN2-x TB) but does not provide specific version numbers for the software dependencies of its own implementation. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix G. |