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..
MDM: Molecular Diffusion Model for 3D Molecule Generation
Authors: Lei Huang, Hengtong Zhang, Tingyang Xu, Ka-Chun Wong
AAAI 2023 | Venue PDF | 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. |