MDM: Molecular Diffusion Model for 3D Molecule Generation
Authors: Lei Huang, Hengtong Zhang, Tingyang Xu, Ka-Chun Wong
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple benchmarks demonstrate that the proposed model significantly outperforms existing methods for both unconditional and conditional generation tasks. |
| Researcher Affiliation | Collaboration | Lei Huang1, 2*, Hengtong Zhang2 , Tingyang Xu2, Ka-Chun Wong1 1 City University of Hong Kong 2Tencent AI Lab |
| Pseudocode | Yes | Algorithm 1: Training Process Input: The molecular geometry G(A, R), VAE encoder ϕv global equivariant neural networks ϕg, local neural networks ϕl |
| Open Source Code | Yes | The codes are available at https://github.com/tencent-ailab/MDM |
| Open Datasets | Yes | We adopt QM9 (Ramakrishnan et al. 2014) and GEOM-Drugs (Axelrod and Gomez-Bombarelli 2022) to evaluate the performance of MDM. |
| Dataset Splits | No | The paper mentions splitting the QM9 training set for classifier training and generative model training but does not provide general train/validation/test splits for the main experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., libraries, frameworks). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations for MDM. |