Insight into the structural requirements of proton pump inhibitors based on CoMFA and CoMSIA studies

In the present study, a series of 179 quinoline and quinazoline heterocyclic analogues exhibiting inhibitory activity against Gastric (H+/K+)-ATPase were investigated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods. Both the models exhibited good correlation between the calculated 3D-QSAR fields and the observed biological activity for the respective training set compounds. The most optimal CoMFA and CoMSIA models yielded significant leave-one-out cross-validation coefficient, q(2) of 0.777, 0.744 and conventional cross-validation coefficient, r(2) of 0.927, 0.914 respectively. The predictive ability of generated models was tested on a set of 52 compounds having broad range of activity. CoMFA and CoMSIA yielded predicted activities for test set compounds with r(pred)(2) of 0.893 and 0.917 respectively. These validation tests not only revealed the robustness of the models but also demonstrated that for our models r(pred)(2) based on the mean activity of test set compounds can accurately estimate external predictivity. The factors affecting activity were analyzed carefully according to standard coefficient contour maps of steric, electrostatic, hydrophobic, acceptor and donor fields derived from the CoMFA and CoMSIA. These contour plots identified several key features which explain the wide range of activities. The results obtained from models offer important structural insight into designing novel peptic-ulcer inhibitors prior to their synthesis.     

N,N-二甲基-2-溴苯乙胺类衍生物活性与CoMFA、CoMSIA的研究

用比较分子力场分析(CoMFA)法和比较分子相似性指数分析(CoMSIA)法,建立N,N-二甲基-2-溴苯乙胺类化合物的3D-QSAR模型。CoMFA模型中,其交叉验证系数q2=0．792,传统的相关系数R2=0．955(R=0．978),相应立体场贡献为77．4％、静电场贡献为22．6％,优于文献的报导。CoMSIA研究中,其交叉验证系数q2=0．757,传统的相关系数R2=0．917 (R=0．958),其疏水场、立体场、静电场贡献依次为:42．9％、39．5％、17．6％。用两种模型分别预测检测集分子的活性,结果与实验值较吻合。说明所建的模型具有较好的预测能力。通过分析CoMFA分子场等值线图,可为优化N,N-二甲基-2-溴苯乙胺类衍生物的结构提供理论指导。     

分子连接指数法预测硝基苯类化合物的毒性研究

建立可以预测硝基苯类化合物毒性的模型,本文采用分子连接指数法(MCI)计算了57个硝基苯类化合物的各种定量化学参数,并研究了这些量化参数对大鼠口服急性毒性(半数致死量,LD50)的影响.采用多元线性回归选取相关变量,建立大鼠口服急性毒性的预测模型.最终模型为:LogLD50=7.68076*A-11.36188*B-1.601*CCI(n=39,R2=0.9651,F=331.91);其中A是零阶MCI,B是一阶MCI,CCI是核-核间相互作用.经验证该模型具有良好的预测能力.     

一种创新的抗乳腺癌药物分子特征筛选模型

据不完全统计显示,2020年全球乳腺癌新增人数达到226万,女性乳腺癌是最常见的癌症类型,死亡率高居第五,因此对乳腺癌的治疗研究变得愈发重要。对雌激素受体α亚型(ERα)的研究显示其在乳腺发育过程中扮演重要角色。本文收集作用于ERα的化合物及其生物活性数据,并以一系列分子结构描述符作为自变量和以化合物的生物活性值作为因变量,通过随机森林与梯度提升树并融合专家知识来构建分子筛选模型,筛选出前20个对生物活性最具有显著影响的分子描述符。这些分子描述符对指导已有活性化合物的结构优化和药物研究具有重要意义。     

氨基喹啉类抗疟疾药物的活性预测

采用分子全息距离矢量方法描述40个氨基喹啉类化合物的分子结构，运用主成分回归方法建模进行定量构效关系分析，预测其抗疟原虫活性。其两组活性数据所得结果相关系数分别为0．9438和0．9737，交互检验相关系数分别为0．8305和0．9098。由此表明所建立的多参数模型稳定，能较好地预测氨基喹啉类药物的抗疟原虫活性。为指导和设计新的高效低毒抗疟疾药物提供有力依据。