Department of Bioinformatics, Korea University, 2511 Sejong-ro, Sejong 30119, Korea.
VeraChem LLC, 12850 Middlebrook Road STE 205, Germantown, Maryland 20874, United States.
J Chem Inf Model. 2023 May 8;63(9):2728-2734. doi: 10.1021/acs.jcim.2c01637. Epub 2023 Apr 20.
We developed an effective binding free energy prediction protocol which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations to substitute the specified atomic charges of force fields with quantum-mechanically recalculated ones at a proposed pose using a mining minima approach with the VeraChem mining minima engine. We tested this protocol using seven well-known targets with 147 different ligands and compared it with classical mining minima and the most popular binding free energy (BFE) methods using different metrics. Our new protocol, dubbed Qcharge-VM2, yielded an overall Pearson correlation of 0.86, which was better than all the methods examined. Qcharge-VM2 performed significantly better than implicit solvent-based methods, such as MM-GBSA and MM-PBSA, but not as good as explicit water-based free energy perturbation methods, such as FEP+, in terms of root-mean-square error, RMSE (1.75 kcal/mol) and mean unsigned error, MUE (1.39 kcal/mol) on a limited set of targets. However, our protocol is substantially less computationally demanding compared with FEP+. The combined accuracy and efficiency of our method can be valuable in drug discovery campaigns.
我们开发了一种有效的结合自由能预测方案，该方案结合了量子力学/分子力学（QM/MM）计算，使用 VeraChem 挖掘极小值引擎的挖掘极小值方法，用量子力学重新计算了建议构象中力场的指定原子电荷。我们使用七个著名的靶标和 147 种不同的配体测试了该方案，并使用不同的指标与经典挖掘极小值和最流行的结合自由能（BFE）方法进行了比较。我们的新方案称为 Qcharge-VM2，整体 Pearson 相关系数为 0.86，优于所有检查的方法。在有限的目标集上，Qcharge-VM2 在均方根误差（RMSE，1.75 kcal/mol）和平均未签名误差（MUE，1.39 kcal/mol）方面的表现明显优于基于隐溶剂的方法，如 MM-GBSA 和 MM-PBSA，但不如基于显溶剂的自由能微扰方法，如 FEP+。然而，与 FEP+相比，我们的方案计算需求大大降低。我们方法的准确性和效率相结合在药物发现中具有重要价值。
