Equivariant Diffusion for Molecule Generation in 3D
Authors: Emiel Hoogeboom, Vı́ctor Garcia Satorras, Clément Vignac, Max Welling
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time. |
| Researcher Affiliation | Academia | 1Uv A-Bosch Delta Lab, University of Amsterdam, Netherlands 2EPFL, Lausanne, Switzerland. |
| Pseudocode | Yes | Algorithm 1 Optimizing EDM |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating the release of source code for the described methodology. |
| Open Datasets | Yes | QM9 (Ramakrishnan et al., 2014) is a standard dataset that contains molecular properties and atom coordinates for 130k small molecules with up to 9 heavy atoms (29 atoms including hydrogens). |
| Dataset Splits | Yes | We use the train/val/test partitions introduced in (Anderson et al., 2019), which consists of 100K/18K/13K samples respectively for each partition. |
| Hardware Specification | Yes | Training takes approximately 7 days on a single NVIDIA Ge Force GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions software like 'Adam' (optimizer) and 'fully connected neural networks' and general concepts like 'MLPs' and 'EGNN', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | All models use 9 layers, 256 features per layer and Si LU activations. They are trained using Adam with batch size 64 and learning rate 10 4. |