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..
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Authors: Octavian Ganea, Lagnajit Pattanaik, Connor Coley, Regina Barzilay, Klavs Jensen, William Green, Tommi Jaakkola
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we conduct experiments on two benchmarks: GEOM-QM9 (smaller molecules relevant to gas-phase chemistry) and GEOM-DRUGS (drug-like molecules) [Axelrod and Gomez Bombarelli, 2020a]. Our method often outperforms previous ML and two popular open-source or commercial methods in different metrics. |
| Researcher Affiliation | Academia | Department of Chemical Engineering, MIT, Cambridge, MA 02139 Computer Science and Arti๏ฌcial Intelligence Laboratory, MIT, Cambridge, MA 02139 |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented in the paper. |
| Open Source Code | Yes | Code is available at https://github.com/Pattanaik L/Geo Mol. |
| Open Datasets | Yes | We use two popular datasets: GEOM-QM9 [Ramakrishnan et al., 2014] and GEOM-DRUGS [Axelrod and Gomez-Bombarelli, 2020a]. |
| Dataset Splits | Yes | We split them randomly based on molecules into train/validation/test (80%/10%/10%). |
| Hardware Specification | No | No specific hardware specifications (e.g., GPU/CPU models, memory) are mentioned for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | Yes | At train time, our model uses a standard deviation (std) s (see eq. (1)) of 5 for both GEOM-QM9 and GEOM-DRUGS. For GEOM-QM9, this corresponds to no RMSD cutoff (i.e., OMEGA generates all possible conformers), and for GEOM-DRUGS, this corresponds to a cutoff of 0.7ร . We discuss hyper-parameters and additional training details in appendix H. |