Fractional Denoising for 3D Molecular Pre-training
Authors: Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD171. |
| Researcher Affiliation | Academia | 1Institute for AI Industry Research (AIR), Tsinghua University 2Academy of Mathematics and Systems Science, Chinese Academy of Sciences |
| Pseudocode | Yes | For a complete description of our method s pipeline, please refer to Algorithm 1 in Appendix. |
| Open Source Code | Yes | 1The code is released publicly at https://github.com/ fengshikun/Frad |
| Open Datasets | Yes | We leverage a large-scale molecular dataset PCQM4Mv2 (Nakata & Shimazaki, 2017) as our pre-training dataset. |
| Dataset Splits | Yes | The QM9 dataset is split into a training set with 110,000 and a validation set with 10,000 samples, leaving 10,831 samples for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software like RDKit but does not provide specific version numbers for ancillary software dependencies required for replication. |
| Experiment Setup | Yes | Table 9. Hyperparameters for pre-training. |