【作者】 李琳；

【导师】 胡丽红；

【作者基本信息】 东北师范大学 ， 计算机应用技术， 2015， 硕士

【摘要】 非共价键相互作用是分子间的一种弱相互作用,它对生物分子结构、超分子和配体结合反应等至关重要,然而,不仅实验上准确测量非共价键相互作用难度很大,理论上精确的计算非共价键相互作用需要通过量子化学计算方法中高精度的计算方法,如耦合簇方法 CCSD(T)或高阶微扰理论MPn等配合较大的基组的方法来获得,这需要较多的机时和大量的计算资源。为了减少计算成本,同时获得比较精确的结果,本文将广义回归神经网络(generalized regression neural network GRNN)与密度泛函理论(density functional theory DFT)相结合来提高非共价键相互作用的计算精度。本文对P.Hobza教授研究组开发的非共价键基础数据库提供的121个分子体系进行研究。运用六种密度泛函方法 M06-2X、B3LYP、B3LYP-D3、PBE、PBE-D3和ωB97XD配合较小基组(6-31G*和6-31+G*)及水和类蛋白两种溶液对这121个分子体系进行非共价键相互作用计算。利用GRNN在DFT的计算结果基础上得到更贴近数据库提供的CCSD(T)/CBS方法的结果。通过GRNN校正后,计算结果的均方根误差(RMSE)明显减小,最高减少84%[B3LYP/6-31G*(水溶液)],最低也达到68%[M062X/6-31G*(类蛋白溶液)],同时运用OECD准则对实验用到的模型进行拟合度,预测能力,稳定性评价,结果均大于0.9,说明本文的实验模型具有良好的拟合度、预测能力和稳定性,对非共价键的相互作用的校正准确有效。更多还原

【Abstract】 Non-covalent interactions are weak intermolecular interaction that are crucial for bio-molecular structures, super molecules and ligand binding reactions. However, non-covalent interactions are not only experimentally difficult to measure, but also theoretically quite demanding, therefore the advanced ab initio quantum chemical method CCSD(T) and MPn with large basis sets may be required, which cost plenty of machine time and computing resources. To reduce the cost of the calculations so as to perform accurate calculations for non-covalent interactions of large molecular systems, we combine generalized regression neural network GRNN with density functional theory DFT to improve the accuracy of DFT calculations for non-covalent interactions.In this thesis, the calculations of non-covalent interactions are based on P. Hobza’s databases, which include 121 non-covalent binding molecular dimmers. Six DFT methods, M06-2X, B3 LYP, B3LYP-D3, PBE, PBE-D3 and ωB97XD with two small basis sets 6-31G* and 6-31+G* using either water or pentylamine as solvents are set to calculate database molecules. GRNN is used to correct the DFT calculations and obtain results are closer to benchmark interactions calculated by CCSD(T)/CBS method. After correction by GRNN, the root mean square error(RMSE) is significantly reduced, which are decreased at least 68% [M062X/6-31G*(protein)], and the most of reduction is 84%[B3LYP/6-31G*(water)]. The OECD principles are used to evaluate the fitting power, prediction power and stability power of our models. All the validation parameters are larger than 0.9, which means the built models exhibit good stability, robustness and predictivity for non-covalent bond interaction calculations.更多还原