Conditional Antibody Design as 3D Equivariant Graph Translation
Authors: Xiangzhe Kong, Wenbing Huang, Yang Liu
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess our model on the three challenging tasks: 1. The generative task on the Structural Antibody Database (Dunbar et al., 2014) in 4.1; 2. Antigen-binding CDR-H3 design from a curated benchmark of 60 diverse antibody-antigen complexes (Adolf-Bryfogle et al., 2018) in 4.2; 3. Antigen-antibody binding affinity optimization on Structural Kinetic and Energetic database of Mutant Protein Interactions (Jankauskait e et al., 2019) in 4.3. We also present a promising pipeline to apply our model in scenarios where the binding position is unknown in 4.4. |
| Researcher Affiliation | Academia | 1Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua University 2Institute for AI Industry Research (AIR), Tsinghua University 3Beijing Academy of Artificial Intelligence 4Gaoling School of Artificial Intelligence, Renmin University of China 5 Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China |
| Pseudocode | No | The paper describes its methodology through detailed textual descriptions and mathematical equations, but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The codes for our MEAN are available at https://github.com/THUNLP-MT/MEAN. |
| Open Datasets | Yes | The generative task on the Structural Antibody Database (Dunbar et al., 2014) in 4.1; 2. Antigen-binding CDR-H3 design from a curated benchmark of 60 diverse antibody-antigen complexes (Adolf-Bryfogle et al., 2018) in 4.2; 3. Antigen-antibody binding affinity optimization on Structural Kinetic and Energetic database of Mutant Protein Interactions (Jankauskait e et al., 2019) in 4.3. |
| Dataset Splits | Yes | We split the dataset into training, validation, and test sets according to the clustering of CDRs to maintain the generalization test... Then we split all clusters into training, validation, and test sets with a ratio of 8:1:1. We conduct 10-fold cross validation to obtain reliable results. Further details are provided in Appendix A. |
| Hardware Specification | Yes | We conduct experiments on a machine with 56 CPU cores and 10 Ge Force RTX 2080 Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch' for data-parallel framework but does not specify a version number for PyTorch or any other key software dependencies. |
| Experiment Setup | Yes | We use the Adam optimizer with lr = 0.001 and decay the learning rate by 0.95 every epoch. The batch size is set to be 16. All models are trained for 20 epochs and the checkpoint with the lowest loss on the validation set is selected for testing. For MEAN, we run 3 iterations for the progressive full-shot decoding. (Also Table 6 provides detailed hyperparameters for each model). |