Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
Authors: Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on standard paratope docking and design benchmarks (Adolf-Bryfogle et al., 2018). In terms of docking, we compare HSRN against HDOCK (Yan et al., 2020) and combined it with Ig Fold (Ruffolo & Gray, 2022) for end-to-end paratope folding and docking. In terms of generation, we compare HSRN against standard sequence-based generative models and a state-of-the-art structure-based methods (Jin et al., 2021). HSRN significantly outperformed all baselines in both settings, with over 50% absolute improvement in docking success rate. |
| Researcher Affiliation | Academia | 1Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard 2CSAIL, Massachusetts Institute of Technology. |
| Pseudocode | No | The paper describes its methods and algorithms using text and mathematical equations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at github.com/wengong-jin/abdockgen |
| Open Datasets | Yes | Our training data comes from the Structural Antibody Database (SAb Dab) (Dunbar et al., 2014) |
| Dataset Splits | No | The paper mentions the training set size, but it does not specify any explicit validation set splits (e.g., percentages, sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or specific computing environments) used for running the experiments. |
| Software Dependencies | No | The paper mentions using an "Adam optimizer" but does not specify any software names with version numbers for other libraries or dependencies, such as Python, PyTorch, or TensorFlow versions. |
| Experiment Setup | Yes | Each MPN in our hierarchical encoder contains four message passing layers with a hidden dimension of 256. The docking module performs eight iterations of structure refinement. All models are trained by an Adam optimizer for 20 epochs with 10% dropout. |