Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.
NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China.
J Chem Inf Model. 2021 May 24;61(5):2231-2240. doi: 10.1021/acs.jcim.1c00334. Epub 2021 May 12.
In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from docked decoy poses due to the lack of decoy structural information in their training data. Here, we developed a machine-learning model, named DeepBSP, that can directly predict the root mean square deviation (rmsd) of a ligand docking pose with reference to its native binding pose. Unlike the binding affinity, the rmsd between the docking poses with reference to their native structures can be straightforwardly determined. By training on a generated data set with 11,925 native complexes and more than 165,000 docked poses, our model shows excellent docking power on our test set and also on the CASF-2016 docking decoy set compared to other major scoring functions. Thus, by combining molecular dockings that generate many poses with the application of DeepBSP, one can more accurately predict the best binding pose that is closest to the native complex structure. This DeepBSP model shall be very useful in picking out poses close to their natives from many poses generated from a dock application.
近年来，基于机器学习的打分函数显著提高了打分能力。然而，由于这些方法的训练数据中缺乏诱饵结构信息，它们在区分天然结构和对接诱饵构象方面的表现并不理想。在这里，我们开发了一种名为 DeepBSP 的机器学习模型，它可以直接预测配体对接构象与天然结合构象之间的均方根偏差 (rmsd)。与结合亲和力不同，参考天然结构的对接构象之间的 rmsd 可以直接确定。通过在包含 11925 个天然复合物和超过 165000 个对接构象的生成数据集上进行训练，我们的模型在我们的测试集以及与其他主要打分函数相比的 CASF-2016 对接诱饵集上显示出优异的对接能力。因此，通过将生成许多构象的分子对接与 DeepBSP 的应用相结合，人们可以更准确地预测最接近天然复合物结构的最佳结合构象。这种 DeepBSP 模型对于从对接应用程序生成的许多构象中挑选出接近天然构象的构象非常有用。
