在这项工作中,我们提出了一个类似的方法来解决刚性蛋白-蛋白对接问题:DIFFDOCK-PP是一个扩散生成模型,它学习将非结合蛋白质结构转化为结合构象。我们在DIPS上实现了最先进的性能,中位C-RMSD为4.85,优于所有考虑的基线。此外,DIFFDOCK-PP比所有基于搜索的方法运行更快,并为其预测生成可靠的置信度估计。项目地址...
本研究提出了一种基于扩散生成模型的刚性蛋白-蛋白对接方法：DIFFDOCK-PP。该方法通过学习将非结合蛋白质结构转化为结合构象，在DIPS数据集上实现了最先进的性能，中位C-RMSD为4.85，优于所有考虑的基线。此外，DIFFDOCK-PP运行速度快，并为其预测生成可靠的置信度估计。导言 蛋白质通过与其他生物分子相...
Implementation of DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models in PyTorch (ICLR 2023 - MLDD Workshop) - biocad/DiffDock-PP
[DiffDock-PP](https://github.com/ketatam/DiffDock-PP) (rigid protein-protein interactions), [AlphaFold-Multimer](https://github.com/google-deepmind/alphafold) (flexible protein-protein interactions) or [RoseTTAFold2NA](https://github.com/uw-ipd/RoseTTAFold2NA) (protein-nucleic acid interactions...
[DiffDock-PP](https://github.com/ketatam/DiffDock-PP) (rigid protein-protein interactions), [AlphaFold-Multimer](https://github.com/google-deepmind/alphafold) (flexible protein-protein interactions) or [RoseTTAFold2NA](https://github.com/uw-ipd/RoseTTAFold2NA) (protein-nucleic acid interactions...
[DiffDock-PP](https://github.com/ketatam/DiffDock-PP) (rigid protein-protein interactions), [AlphaFold-Multimer](https://github.com/google-deepmind/alphafold) (flexible protein-protein interactions) or [RoseTTAFold2NA](https://github.com/uw-ipd/RoseTTAFold2NA) (protein-nucleic acid interactions...