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 [1].
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. |