Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.

IF 5.5 1区 化学 Q2 CHEMISTRY, PHYSICAL
Journal of Chemical Theory and Computation Pub Date : 2025-03-25 Epub Date: 2025-03-03 DOI:10.1021/acs.jctc.5c00046
Anastasia Croitoru, Anmol Kumar, Jean-Christophe Lambry, Jihyeon Lee, Suliman Sharif, Wenbo Yu, Alexander D MacKerell, Alexey Aleksandrov
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引用次数: 0

Abstract

Small molecule empirical force fields (FFs), including the CHARMM General Force Field (CGenFF), are designed to have wide coverage of organic molecules and to rapidly assign parameters to molecules not explicitly included in the FF. Assignment of parameters to new molecules in CGenFF is based on a trained bond-angle-dihedral charge increment linear interpolation scheme for the partial atomic charges along with bonded parameters assigned based on analogy using a rules-based penalty score scheme associated with atom types and chemical connectivity. Accordingly, the accuracy of CGenFF is related to the extent of the training set of available parameters. In the present study that training set is extended by 1390 molecules selected to represent connectivities new to CGenFF training compounds. Quantum mechanical (QM) data for optimized geometries, bond, valence angle, and dihedral angle potential energy scans, interactions with water, molecular dipole moments, and electrostatic potentials were used as target data. The resultant bonded parameters and partial atomic charges were used to train a new version of the CGenFF program, v5.0, which was used to generate parameters for a validation set of molecules, including drug-like molecules approved by the FDA, which were then benchmarked against both experimental and QM data. CGenFF v5.0 shows overall improvements with respect to QM intramolecular geometries, vibrations, dihedral potential energy scans, dipole moments and interactions with water. Tests of pure solvent properties of 216 molecules show small improvements versus the previous release of CGenFF v2.5.1 reflecting the high quality of the Lennard-Jones parameters that were explicitly optimized during the initial optimization of both the CGenFF and the CHARMM36 force field. CGenFF v5.0 represents an improvement that is anticipated to more accurately model intramolecular geometries and strain energies as well as noncovalent interactions of drug-like and other organic molecules.

利用扩展训练集提高CHARMM通用力场的准确性和鲁棒性。
小分子经验力场(FFs),包括CHARMM通用力场(CGenFF),旨在广泛覆盖有机分子,并快速将参数分配给未明确包含在FF中的分子。在CGenFF中,新分子的参数赋值是基于一个训练好的键角-二面体电荷增量线性插值方案,该方案对部分原子电荷进行赋值,同时基于一个与原子类型和化学连性相关的基于规则的惩罚评分方案进行类比赋值。因此,CGenFF的准确性与可用参数训练集的范围有关。在本研究中,训练集被扩展了1390个分子,这些分子被选择来代表CGenFF训练化合物的新连接。优化几何、键、价角和二面角势能扫描、与水的相互作用、分子偶极矩和静电势的量子力学(QM)数据被用作目标数据。得到的键合参数和部分原子电荷用于训练新版CGenFF程序v5.0,该程序用于生成分子验证集的参数,包括FDA批准的类药物分子,然后根据实验和QM数据对其进行基准测试。CGenFF v5.0在QM分子内几何形状、振动、二面体势能扫描、偶极矩和与水的相互作用方面进行了全面改进。216种分子的纯溶剂性质测试表明,与之前的CGenFF v2.5.1版本相比,该版本有了微小的改进,这反映了在CGenFF和CHARMM36力场的初始优化过程中明确优化的Lennard-Jones参数的高质量。CGenFF v5.0代表了一项改进,预计将更准确地模拟分子内几何形状和应变能,以及类药物和其他有机分子的非共价相互作用。
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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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