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.
FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames
Authors: Ruidong Wu, Ruihan Guo, Rui Wang, Shitong Luo, Yue Xu, Jiahan Li, Jianzhu Ma, Qiang Liu, Yunan Luo, Jian Peng
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 24,012 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,284 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 | By fine-tuning AF2 with our proposed new loss function, we attain a correct rate of 52.3% (Dock Q > 0.23) on an evaluation set and 43.8% correct rate on a subset with low homology, with substantial improvement over AF2 by 182% and 100% respectively. |
| Researcher Affiliation | Collaboration | 1Helixon 2Tsinghua 3UTAustin 4Ga Tech. |
| Pseudocode | Yes | We provide an example implementation of F2E in Py Torch 2.0.1. (Appendix B.4 contains structured Python code snippets which serve as pseudocode for implementation). |
| Open Source Code | Yes | Code is available at https://github.com/mooninrain/FAFE.git. |
| Open Datasets | Yes | The fine-tuning dataset is collected from The Structural Antibody Database (Sab Dab) (Dunbar et al., 2014), which provides annotations on the original PDB database (Burley et al., 2017) raw structures. |
| Dataset Splits | No | The paper mentions a "training set" and "evaluation set" but does not specify the exact percentages or counts for a training/validation/test split, or reference predefined splits for this partitioning. |
| Hardware Specification | No | The paper mentions "computing resource constraints" but does not specify any particular hardware components like CPU or GPU models used for the experiments. |
| Software Dependencies | Yes | We provide an example implementation of F2E in Py Torch 2.0.1. |
| Experiment Setup | Yes | The batch size is set to 32, which we find is the minimal batch size to improve AF2 performance during fine-tuning. ... The crop length is set to 384 at max. The number of MSA cluster center is restricted to 128 for training efficiency. |