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..
Generating 3D Molecules for Target Protein Binding
Authors: Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji
ICML 2022 | Venue PDF | 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. |