Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Torsional Diffusion for Molecular Conformer Generation
Authors: Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola
NeurIPS 2022 | Venue PDF | 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. |