该研究团队提出一种高效方法，利用经济实惠的参考势如力场(FFs)从模拟中计算量子力学（QM）自由能。由于从传统上计算FF到QM势的修正收敛速度较慢，这一任务通常难以实现。为了攻克这一难题，研究人员采用了定向自由能方法，并使用由正则化流神经网络(nn)实现的多个映射来最大化分布之间的重叠。重要的是，该方法既无需单独的昂贵神经网络训练阶段，也不需要QM势的样本。研究人员还提出了一种one-epoch学习策略以有效避免过拟合，并结合增强的采样策略，克服了由于自由度慢而导致的普遍收敛性差的问题。
在HiPen数据集中的药物类似物分子上，与标准自由能扰动相比，该方法将FF转换为DFTB3势的自由能差的计算速度提高了三个数量级，与之前发布的非平衡计算相比，自由能差的计算速度提高了8倍。这一研究结果表明，该方法可与高效的QM/MM计算结合使用，在药物发现过程中优化主导物，并深入研究蛋白质配体分子识别过程。
据悉，准确预测配体结合亲和力可以大大促进药物发现活动的第一阶段。然而，在自由能计算中使用基于量子力学(QM)的高精度原子间势，由于其巨大的计算成本，迄今为止在实际应用中仍面临很大的困难。
附：英文原文
Title: Free energies at QM accuracy from force fields via multimap targeted estimation
Author: Rizzi, Andrea, Carloni, Paolo, Parrinello, Michele
Issue&Volume: 2023-11-6
Abstract: Accurate predictions of ligand binding affinities would greatly accelerate the first stages of drug discovery campaigns. However, using highly accurate interatomic potentials based on quantum mechanics (QM) in free energy methods has been so far largely unfeasible due to their prohibitive computational cost. Here, we present an efficient method to compute QM free energies from simulations using cheap reference potentials, such as force fields (FFs). This task has traditionally been out of reach due to the slow convergence of computing the correction from the FF to the QM potential. To overcome this bottleneck, we generalize targeted free energy methods to employ multiple maps—implemented with normalizing flow neural networks (NNs)—that maximize the overlap between the distributions. Critically, the method requires neither a separate expensive training phase for the NNs nor samples from the QM potential. We further propose a one-epoch learning policy to efficiently avoid overfitting, and we combine our approach with enhanced sampling strategies to overcome the pervasive problem of poor convergence due to slow degrees of freedom. On the drug-like molecules in the HiPen dataset, the method accelerates the calculation of the free energy difference of switching from an FF to a DFTB3 potential by three orders of magnitude compared to standard free energy perturbation and by a factor of eight compared to previously published nonequilibrium calculations. Our results suggest that our method, in combination with efficient QM/MM calculations, may be used in lead optimization campaigns in drug discovery and to study protein-ligand molecular recognition processes.
DOI: 10.1073/pnas.2304308120
Source: https://www.pnas.org/doi/abs/10.1073/pnas.2304308120