Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fractional Denoising for 3D Molecular Pre-training
Authors: Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 31,147 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,362 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |