Exploring phase-transfer catalysis with molecular dynamics and 3D/4D quantitative structure-selectivity relationships

Quantitative Structure-Selectivity Relationships (QSSR) are developed for a library of 40 phase-transfer asymmetric catalysts, based around quaternary ammonium salts, using Comparative Molecular Field Analysis (CoMFA) and closely related variants. Due to the flexibility of these catalysts, we use molecular dynamics (MD) with an implicit Generalized Born solvent model to explore their conformational space. Comparison with crystal data indicates that relevant conformations are obtained and that, furthermore, the correct biphenyl twist conformation is predicted, as illustrated by the superiority of the resulting model (leave-one-out q(2) = 0.78) compared to a random choice of low-energy conformations for each catalyst (average q(2) = 0.22). We extend this model by incorporating the MD trajectory directly into a 4D QSSR and by Boltzmann-weighting the contribution of selected minimized conformations, which we refer to as '3.5D' QSSR. The latter method improves on the predictive ability of the 3D QSSR (leave-one-out q(2) = 0.83), as confirmed by repeated training/test splits.     

磺酰脲类化合物除草活性的QSAR研究

采用密度泛函理论方法, 在B3LYP/6-31G(d)水平下, 计算了23种磺酰类化合物的分子极化率及分子骨架中各原子的Milliken电荷. 提出了一种新的QSAR建模方法, 并据此对其中18种化合物进行多元线性回归分析, 建立了除草活性的预测模型(R=0.96, R2=0.92, r2adj=0.88, F=26.26, q2=0.71, p<0.01, SE=0.36), 对剩余五种化合物进行预测, 结果吻合. 该模型从化合物的亲水性、分子几何特征的角度对如何提高磺酰脲类化合物的除草活性进行了分析, 并对提高化合物除草活性的方法做出预测: 提高苯环和嘧啶环取代基的亲水性, 增加N13周围的电子云密度, 为苯环接入较小的取代基团, 在嘧啶环上接入较大取代基团都可提高化合物的除草活性. 预测结果与3D-QSAR方法的预测结果一致.     

Quantum mechanical models correlating structure with selectivity: predicting the enantioselectivity of beta-amino alcohol catalysts in aldehyde alkylation

Quantitative structure selectivity relationship (QSSR) models are described that provide consistently reliable predictions for the asymmetric addition of Et2Zn to PhCHO catalyzed by beta-amino alcohols. Statistically valid two-variable linear regression models that correlate the structures of the chiral catalysts with their enantioselectivities are obtained from three-dimensional physical property grids. The strength of the present method is that statistical models obtained from a small set of experimentally determined selectivities and relatively simple theoretical calculations yield selectivity predictions that are as accurate as those derived from higher-level calculations of transition-structure energies. Only minutes of computing time are required. Simple models are obtained which permit straightforward physical interpretation and generate realistic predictions.     

基于概念密度泛函理论磷酸酯类反应性物质毒性预测

磷酸酯类反应性物质是乙酰胆碱酯酶不可逆抑制剂。本文应用概念密度泛函理论(CDFT),采用四组条件(B3LYP/6-311++G(2d, 3p)/gas,B3LYP/6-311++G(2d, 3p)/CPCM/water,MP2/6-311++G(2d, 3p)/gas,MP2/6-311++ G(2d, 3p)/CPCM/water),对20多个磷酸酯反应性物质进行反应性描述指数计算,包括分子的化学势μ,绝对硬度η、亲电性指数ω、分子的前线轨道能量等分子整体描述参数,以及原子福井函数、自然键轨道(NBO)电荷、Wiberg键级、NBO键级等分子局域描述参数。通过对反应性描述指数以及定量构性关系(QSPR)方程预测结果的比较分析,得出结论：大多数化合物亲电进攻的反应中心发生在磷原子上;磷酸酯类化合物侧链乙胺基叔氮的质子化,将显著增强反应中心磷原子的亲电进攻能力;B3LYP/6-311++G(2d, 3p)/gas为最合理的计算条件;应用反应性描述指数建立的QSPR模型明显优于常规的2D-QSPR模型,能够用于乙酰胆碱酯酶不可逆抑制剂的精确毒性预测。     

无环醇~(13)C NMR化学位移与其结构参数的定量关系

用新颖的原子拓扑矢量Y_C、原子平衡电负性q_e、结构信息参数[N_H~i(i=α,β)]和γ校正参数对63个无环饱和脂肪醇的局部化学微环境进行了结构表征,并对化合物~(13)C NMR化学位移进行了QSSR研究.采用偏最小二乘回归得到模型的复相关系数R和标准偏差S分别为0.9915和2.4827;对353个碳原子~(13)C NMR化学位移的实验值与计算值的平均绝对误差仅为2.01×10~(-6).同时,采用留分法(Leave-molecule-out)和外检验方法测试模型的内部稳定性和外部预测能力.与文献结果比较,本研究所用参数少,且计算简便.