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..
Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
Authors: Zhilin Huang, Ling Yang, Xiangxin Zhou, Zhilong Zhang, Wentao Zhang, Xiawu Zheng, Jie Chen, Yu Wang, Bin CUI, Wenming Yang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on Cross Docked2020 dataset show IPDIFF can generate molecules with more realistic 3D structures and state-of-the-art binding affinities towards the protein targets, with up to -6.42 Avg. Vina Score, while maintaining proper molecular properties. https://github.com/Yang Ling0818/IPDiff |
| Researcher Affiliation | Academia | 1 Shenzhen International Graduate School, Tsinghua University 2 Peng Cheng Laboratory 3 Peking University 4 University of Chinese Academy of Sciences 5 Xiamen University |
| Pseudocode | Yes | Algorithm 1 Training Procedure of IPDIFF |
| Open Source Code | Yes | https://github.com/Yang Ling0818/IPDiff |
| Open Datasets | Yes | For molecular generation, following the previous work Luo et al. (2021); Peng et al. (2022); Guan et al. (2023a), we train and evaluate IPDIFF on the Cross Docked2020 dataset (Francoeur et al., 2020). |
| Dataset Splits | No | The paper states '100,000 protein-ligand pairs are utilized for training and 100 proteins for testing' for the Cross Docked2020 dataset but does not explicitly provide details for a validation split. |
| Hardware Specification | Yes | We train IPNET on a single NVIDIA V100 GPU, and we use the Adam as our optimizer with learning rate 0.001, betas = (0.95, 0.999), batch size 16. |
| Software Dependencies | No | The paper mentions 'Adam as our optimizer' and 'AutoDock Vina', but does not provide specific version numbers for software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | Following Guan et al. (2023a), we use the Adam as our optimizer with learning rate 0.001, betas = (0.95, 0.999), batch size 4 and clipped gradient norm 8. We balance the atom type loss and atom position loss by multiplying a scaling factor λ = 100 on the atom type loss. During the training phase, we add a small Gaussian noise with a standard deviation of 0.1 to protein atom coordinates as data augmentation. |