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.

目的和目的：磺酰胺（磺胺类药物）是具有广泛生物活性的化合物，它们是几类药物的基础。推导了定量结构-性质关系（QSPR）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，分别占数据点的70％，15％和15％。使用Dragon软件计算了大量的分子描述符。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QSPR模型。使用Gaussian 98软件在B3LYP / 6-31G *水平上优化了磺胺药物的可能分子几何结构。从Dragon软件衍生的分子描述符用于建立预测模型的预测模型，用于预测所提及化合物的logP。应用遗传算法（GA）方法来选择最相关的分子描述符。

结果：MLR模型的R2和MSE值分别计算为0.312和5.074。对于ANN模型的训练集，测试集和验证集，R2系数分别为0.9869、0.9944和0.9601。

结论：结果比较表明，GA-ANN方法的应用效果优于GA-MLR方法。