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Prediction of Acidity Constants of Thiazolidine-4-carboxylic Acid Derivatives Using Ab Initio and Genetic Algorithm-Partial Least Squares

机译:从头算和遗传算法-偏最小二乘预测噻唑烷-4-羧酸衍生物的酸度常数

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摘要

A quantitative structure-property relationship study is suggested for the prediction of the acidity constants of some thiazolidine-4-carboxylic acid derivatives in aqueous solution.Ab initio theory was used to calculate some quantum chemical descriptors,including electrostatic potentials and local charges at each atom,highest occupied molecular orbital(HOMO)and lowest unoccupied molecular orbital(LUMO)energies,etc.Modeling of the acidity constant of thiazolidine-4-carboxylic acid derivatives as a function of molecular structures was established by means of the partial least squares algorithm.The subset of descriptors,which resulted in a low prediction error,was selected by genetic algorithm.This model was applied for the prediction of the acidity constant of some thiazolidine-4-carboxylic acid derivatives,which were not in the modeling procedure.Relative errors of prediction lower than 1.5% were obtained by using the genetic algorithm-partial least squares(GA-PLS)method.The developed model has good prediction ability with a root mean square error of prediction of 0.0419 and 0.1013 for PLS and GA-PLS models,respectively.
机译:建议采用定量结构-性质关系研究预测某些噻唑烷-4-羧酸衍生物在水溶液中的酸度常数。采用从头算理论计算一些量子化学描述子,包括每个原子的静电势和局部电荷通过部分最小二乘算法,建立了噻唑烷-4-羧酸衍生物的酸度常数随分子结构变化的函数模型,建立了最高占据分子轨道(HOMO)和最低未占据分子轨道(LUMO)能量。通过遗传算法选择了描述子的子集,该子集的预测误差很小。该模型用于预测一些未在建模过程中的噻唑烷-4-羧酸衍生物的酸度常数。遗传算法-偏最小二乘(GA-PLS)方法获得的预测值低于1.5%。 odel具有良好的预测能力,对于PLS和GA-PLS模型,预测的均方根误差分别为0.0419和0.1013。

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