Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization
Authors: Ruijia Wang, YiWu Sun, Yujie Luo, Shaochuan Li, Cheng Yang, Xingyi Cheng, Hui Li, Chuan Shi, Le Song
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on diverse datasets and evaluation protocols validate the effectiveness of Bi Dock. Compared to state-of-the-art baselines, Bi Dock achieves the maximum 234% relative improvement in challenging but practical antibody-antigen docking. |
| Researcher Affiliation | Collaboration | Ruijia Wang12 Yiwu Sun1 Yujie Luo1 Shaochuan Li1 Cheng Yang2 Xingyi Cheng1 Hui Li1 Chuan Shi2 Le Song1 1 Bio Map Research 2 Beijing University of Posts and Telecommunications {wangruijia, yangcheng, shichuan}@bupt.edu.cn, {yiwu, luoyujie, shaochuan, xingyi, lihui, songle}@biomap.com |
| Pseudocode | No | The paper contains mathematical formulations and descriptions but no explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions utilizing pretrained models for baselines (Equi Dock, Multimer) available on GitHub, but it does not provide a link or explicit statement about the open-source code for its proposed method, Bi Dock. |
| Open Datasets | Yes | We leverage Docking Benchmark 5.5 (DB5.5) [52], a gold standard dataset tailored for rigid docking. For a comprehensive comparison, we curate two datasets of antibodies (VH-VL) and antibody-antigen complexes (AB-AG) from Protein Data Bank (PDB) [3] and expect them to become new benchmarks. |
| Dataset Splits | No | The paper describes training and test set partitions ('the training set consists of 4,890 complexes', 'the test set comprises 68 antibody-antigen complexes') but does not explicitly state the use or size of a validation set split for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | The environment where we run all experiments is: Operating system: Linux version 5.13.0-30-generic CPU information: AMD EPYC 7742 64-Core Processor GPU information: NVIDIA A100-SXM4-80GB |
| Software Dependencies | No | The paper mentions using the 'Torch Opt' library and 'Multimer' checkpoint, but it does not specify version numbers for these or any other key software dependencies required for replication. |
| Experiment Setup | Yes | The crop size is set to 412, and the batch size is set to 1. The coefficients in Equation (13) are λ1 = 0.2, λ2 = 2.0, and λ3 = 10.0. For optimization, we employ the Adam optimizer with a learning rate of 10 4 and integrate learning rate warmup, gradually increasing the learning rate from 0 to 10 4 within the first 100 steps. The exponential moving average (EMA) strategy applies a decay rate of β = 0.999 and undergoes updates every 200 steps. |