Generating 3D Molecules for Target Protein Binding
Authors: Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our Graph BP is effective to generate 3D molecules with binding ability to target protein binding sites. Our implementation is available at https://github.com/divelab/Graph BP. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science & Engineering, Texas A&M University, TX, USA 2Fujitsu Research of America, INC., CA, USA 3Fujitsu Research, Fujitsu Limited, Kanagawa, Japan. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/divelab/Graph BP. |
| Open Datasets | Yes | We use the Cross Docked2020 dataset (Francoeur et al., 2020), which contains over 22 million docked proteinligand crystal structures, to evaluate Graph BP for structure-based drug design. |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' but does not explicitly state a 'validation set' or 'validation split'. |
| Hardware Specification | No | The paper mentions 'insufficient memory budget' as a reason for not using more advanced GNNs, but it does not specify any particular hardware (e.g., GPU models, CPU types, or cloud instances) used for the experiments. |
| Software Dependencies | No | The paper mentions software like 'RDkit', 'Open Babel', 'gnina', and 'PyTorch' (implicitly), but it does not provide specific version numbers for any of these software components. |
| Experiment Setup | No | The paper mentions 'stochastic gradient descent' for training and the general architecture, but it does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings. |