Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
Authors: Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola
ICML 2022 | Venue PDF | 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. |