Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
Authors: Can Xu, Haosen Wang, Weigang Wang, Pengfei Zheng, Hongyang Chen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on current benchmarks demonstrate the superiority of GFMDiff. |
| Researcher Affiliation | Collaboration | Can Xu1,2* , Haosen Wang3,2*, Weigang Wang1 , Pengfei Zheng2, Hongyang Chen2 1Zhejiang Gongshang University 2Zhejiang Lab 3Southeast University leoxc1571@163.com, haosenwang@seu.edu.cn, wangweigang@zjgsu.edu.cn, zpf2021@zhejianglab.com, dr.h.chen@ieee.org |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided or include a link to a repository. |
| Open Datasets | Yes | we conduct experiments on two benchmark datasets in molecule generation: GEOM-QM9 (Ramakrishnan et al. 2014) and GEOM-Drugs (Axelrod and Gomez-Bombarelli 2022). |
| Dataset Splits | No | The paper mentions 'train', 'validation', and 'test' in general terms for experimental phases but does not provide specific percentages, sample counts, or explicit details about how the datasets were split for training, validation, and testing to enable reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | On QM9, GFMDiff is trained for around 1000 epochs, with a five layer DTN and the embedding size of 256. ... The weight for GFLoss λ is set 0.01. |