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稳健高效的隐式溶剂化模型用于快速半经验方法。

Robust and Efficient Implicit Solvation Model for Fast Semiempirical Methods.

机构信息

Mulliken Center of Theoretical Chemistry, University of Bonn, Bonn 53115, Germany.

出版信息

J Chem Theory Comput. 2021 Jul 13;17(7):4250-4261. doi: 10.1021/acs.jctc.1c00471. Epub 2021 Jun 29.

DOI:10.1021/acs.jctc.1c00471
PMID:34185531
Abstract

We present a robust and efficient method to implicitly account for solvation effects in modern semiempirical quantum mechanics and force fields. A computationally efficient yet accurate solvation model based on the analytical linearized Poisson-Boltzmann (ALPB) model is parameterized for the extended tight binding (xTB) and density functional tight binding (DFTB) methods as well as for the recently proposed GFN-FF general force field. The proposed methods perform well over a broad range of systems and applications, from conformational energies over transition-metal complexes to large supramolecular association reactions of charged species. For hydration free energies of small molecules, GFN1-xTB(ALPB) is reaching the accuracy of sophisticated explicitly solvated approaches, with a mean absolute deviation of only 1.4 kcal/mol compared to the experiment. Logarithmic octanol-water partition coefficients (log ) are computed with a mean absolute deviation of about 0.65 using GFN2-xTB(ALPB) compared to experimental values indicating a consistent description of differential solvent effects. Overall, more than twenty solvents for each of the six semiempirical methods are parameterized and tested. They are readily available in the xtb and dftb+ programs for diverse computational applications.

摘要

我们提出了一种强大而高效的方法,可以在现代半经验量子力学和力场中隐式考虑溶剂化效应。我们基于解析线性化泊松-玻尔兹曼(ALPB)模型为扩展紧束缚(xTB)和密度泛函紧束缚(DFTB)方法以及最近提出的 GFN-FF 通用力场参数化了一种计算效率高但准确的溶剂化模型。所提出的方法在广泛的系统和应用中表现良好,从构象能到过渡金属配合物,再到带电荷物种的大超分子缔合反应。对于小分子的水合自由能,GFN1-xTB(ALPB) 达到了复杂的显式溶剂化方法的精度,与实验值相比平均绝对偏差仅为 1.4 kcal/mol。与实验值相比,使用 GFN2-xTB(ALPB) 计算的对数辛醇-水分配系数(log )的平均绝对偏差约为 0.65,表明对不同溶剂效应的一致描述。总体而言,对于六种半经验方法中的每一种,都参数化和测试了超过二十种溶剂。它们可在 xtb 和 dftb+ 程序中用于各种计算应用。

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