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。     

基于分子参数的药物小肠吸收预测模型

选择100个化合物作为数据集,随机选取其中80个为训练集,其他分子为验证集,并为每个化合物分子计算了30个参数.通过采用五种不同多元线性回归分析方法对其训练模拟,建立了数学模型,并用验证集检验了所建模型的预测能力.结果发现向后筛选法为最优小肠吸收建模方法.由该法所建模型的统计结果良好(R2>0.80),应用于验证集时也表现出较强预测能力.该模型确定了对小肠吸收影响较大的分子参数,有助于指导进一步的新药筛选和开发.     

Prediction of acute toxicity of organophosphorus pesticides using topological indices

Topological indices were used in the prediction of the acute toxicity (intraperitoneal and oral LD₅₀) of organophosphorus pesticides on rats. Models with six variables for the prediction of LD₅₀-i.p. (r?=?0.849, Q ²?=?0.613) and eight variables for LD₅₀-oral (r?=?0.906, Q ²?=?0.701) were selected. External group and cross-validation by use of leave-n-out tests were also performed in order to assess the stability and the prediction performance of the selected topological models.     

脂肪醇的味阈值与分子结构之间的定量关系

通过拓扑方法探讨了脂肪醇的味阈值与分子结构之间的关系,发现脂肪醇的味阈值主要取决于分子的大小和羟基在分子中的位置(且羟基的位置对味阈值的影响十分显著),提出一个既能表征脂肪醇结构与味阈值之间关系、又能预测味阈值的定量关系式,发展了一种设计具有特定味阈值的脂肪醇分子结构的方法。对99种脂肪醇的计算结果表明,味阈值预测值与计算值与实验值的一致性令人满意。     

Prediction of drug solubility from molecular structure using a drug-like training set

Using a training set of 191 drug-like compounds extracted from the AQUASOL database a quantitative structure-property relationship (QSPR) study was conducted employing a set of simple structural and physicochemical properties to predict aqueous solubility. The resultant regression model comprised five parameters (ClogP, molecular weight, indicator variable for aliphatic amine groups, number of rotatable bonds and number of aromatic rings) and demonstrated acceptable statistics (r ² = 0.87, s = 0.51, F = 243.6, n = 191). The model was applied to two test sets consisting of a drug-like set of compounds (r ² = 0.80, s = 0.68, n = 174) and a set of agrochemicals (r ² = 0.88, s = 0.65, n = 200). Using the established general solubility equation (GSE) on the training and drug-like test set gave poorer results than the current study. The agrochemical test set was predicted with equal accuracy using the GSE and the QSPR equation. The results of this study suggest that increasing molecular size, rigidity and lipophilicity decrease solubility whereas increasing conformational flexibility and the presence of a non-conjugated amine group increase the solubility of drug-like compounds. Indeed, the proposed structural parameters make physical sense and provide simple guidelines for modifying solubility during lead optimisation.