使用遗传算法-多元线性回归和遗传算法-人工神经网络方法研究一些磺胺类药物的辛醇-水分配系数的结构关系。

Structural Relationship Study of Octanol-Water Partition Coefficient of Some Sulfa Drugs Using GA-MLR and GA-ANN Methods.

机构信息

Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran.

出版信息

Curr Comput Aided Drug Des. 2020;16(3):207-221. doi: 10.2174/1573409915666190301124714.

DOI:10.2174/1573409915666190301124714

PMID:32507103

Abstract

AIM AND OBJECTIVE

Sulfonamides (sulfa drugs) are compounds with a wide range of biological activities and they are the basis of several groups of drugs. Quantitative Structure-Property Relationship (QSPR) models are derived to predict the logarithm of water/ 1-octanol partition coefficients (logP) of sulfa drugs.

MATERIALS AND METHODS

A data set of 43 sulfa drugs was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GAANN) were employed to design the QSPR models. The possible molecular geometries of sulfa drugs were optimized at B3LYP/6-31G* level with Gaussian 98 software. The molecular descriptors derived from the Dragon software were used to build a predictive model for prediction logP of mentioned compounds. The Genetic Algorithm (GA) method was applied to select the most relevant molecular descriptors.

RESULTS

The R2 and MSE values of the MLR model were calculated to be 0.312 and 5.074 respectively. R2 coefficients were 0.9869, 0.9944 and 0.9601for the training, test and validation sets of the ANN model, respectively.

CONCLUSION

Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method.

摘要

目的和目标

磺胺类药物（磺胺类药物）是具有广泛生物活性的化合物，它们是几组药物的基础。衍生出定量构效关系（QSAR）模型来预测磺胺类药物的水/ 1-辛醇分配系数（logP）的对数。

材料和方法

将 43 种磺胺类药物的数据集随机分为 3 组：训练集、测试集和验证集，分别包含 70%、15%和 15%的数据点。使用 Dragon 软件计算了大量分子描述符。遗传算法 - 多元线性回归（GA-MLR）和遗传算法 - 人工神经网络（GAANN）用于设计 QSAR 模型。使用 Gaussian 98 软件在 B3LYP/6-31G* 水平上优化磺胺类药物的可能分子几何形状。从 Dragon 软件导出的分子描述符用于构建用于预测所述化合物的 logP 的预测模型。应用遗传算法（GA）方法选择最相关的分子描述符。