Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials
Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials
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DOI：
10.1021/acs.jctc.1c01043
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发表时间：
2022-01-12
影响因子：
5.5
通讯作者：
Wang, Junmei
中科院分区：
文献类型：
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作者：
Hao, Dongxiao;He, Xibing;Wang, Junmei
需要一种高效而准确的方法来产生大量分子机械力场（MMFF）参数化的能量数据，特别是对于扭转角参数，这些参数通常是为了再现从头开始的旋转轮廓或扭转势能表面（PES）而导出的。最近，开发了一种有机分子的主动学习潜力（ANI-1x），它可以产生光滑且具有物理意义的 PES。高效率和高精度使 ANI-1x 对于低成本的几何优化特别有吸引力。为了在 MMFF 参数化中应用 ANI-1x 潜力，需要执行约束几何优化。在这项工作中，我们首先开发了一种计算协议来约束其几何形状由笛卡尔坐标描述的分子的可旋转扭转角和其他几何参数。通过两种共轭梯度 (CG) 算法的力投影成功实现了约束。然后，我们对 ANI-1x 以及四种不同的优化算法进行了大规模评估，以重现两种 CG 算法（CG 回溯线搜索 (CG-BS) 和 CG Wolfe 线搜索 (CG-WS)）以及两种准牛顿算法（Broyden-Fletcher-Goldfarb-Shanno (BFGS) 和低内存 BFGS (L-BFGS)）的 DFT 能量和几何形状。请注意，CG-BS 是我们在这项工作中开发的一种新算法。所有四种算法均采用 ANI 能量和力来优化分子几何形状。最后，我们从三个方面对ANI-1x在MMFF开发中的应用进行了大规模评估。首先，我们对 100 个药物分子进行了全面优化，每个分子由 5 种不同的构象组成。 ANI-1x和DFT之间的平均均方根误差（RMSE）约为1.3 kcal/mol，重原子的均方根位移（RMSD）约为0.35埃。其次，我们生成了 160 个有机分子的扭转 PES，并对每个 PES 的多达 18 个构象进行了约束优化。我们发现所有构象异构体的 RMSE 为 1.23 kcal/mol。最后，我们对丙氨酸二肽进行了约束优化，其中 phi 和 phi 角均被冻结。 Ramachandran 图表明，两种 CG 算法与 ANI-1x 势相结合可以很好地重现 DFT 优化的几何结构和扭转 PES。我们得出的结论是，CG-BS 和 CG-WS 是生成 PES 的不错选择，而 CG-WS 或 BFGS 是执行完整几何优化的理想选择。随着 ANI 质量的不断提高，预计本工作中提出的计算算法和协议将在提高现有小分子 MMFF 的质量方面有很大的应用。
An efficient yet accurate method for producing a large amount of energy data for molecular mechanical force field (MMFF) parameterization is on demand, especially for torsional angle parameters which are typically derived to reproduce ab initio rotational profiles or torsional potential energy surfaces (PESs). Recently, an active learning potential (ANI-1x) for organic molecules which can produce smooth and physically meaningful PESs has been developed. The high efficiency and accuracy make ANI-1x especially attractive for geometry optimization at low cost. To apply the ANI-1x potential in MMFF parameterization, one needs to perform constrained geometry optimization. In this work, we first developed a computational protocol to constrain rotatable torsional angles and other geometric parameters for a molecule whose geometry is described by Cartesian coordinates. The constraint is successfully achieved by force projection for the two conjugated gradient (CG) algorithms. We then conducted large-scale assessments on ANI-1x along with four different optimization algorithms in reproducing DFT energies and geometries for two CG algorithms, CG backtracking line search (CG-BS) and CG Wolfe line search (CG-WS), and two quasi-Newton algorithms, Broyden-Fletcher-Goldfarb-Shanno (BFGS) and low-memory BFGS (L-BFGS). Note that CG-BS is a new algorithm we developed in this work. All four algorithms take the ANI energies and forces to optimize a molecule geometry. Last, we conducted a large-scale assessment of applying ANI-1x in MMFF development in three aspects. First, we performed full optimizations for 100 drug molecules, each consisting of five distinct conformations. The average root-mean-square error (RMSE) between ANI-1x and DFT is about 1.3 kcal/mol, and the root-mean-square displacement (RMSD) of heavy atoms is about 0.35 angstrom. Second, we generated torsional PESs for 160 organic molecules, and constrained optimizations were performed for up to 18 conformations for each PES. We found that the RMSE of all the conformers is 1.23 kcal/mol. Last, we carried out constrained optimizations for alanine dipeptide with both phi and phi angles being frozen. The Ramachandran plots indicate that the two CG algorithms in conjunction with the ANI-1x potential could well reproduce the DFT-optimized geometries and torsional PESs. We concluded that CG-BS and CG-WS are good choices for generating PESs, while CG-WS or BFGS is ideal for performing full geometry optimization. With the continuously increased quality of ANI, it is expected that the computational algorithms and protocols presented in this work will have great applications in improving the quality of an existing small-molecule MMFF.