The 1,3,4-thiadiazoles are among the structural moieties that were found to be of utmost importance in the fields of pharmacy and agrochemicals because of their widespread biological activity that includes anti-tumor, antibacterial, anti-inflammatory, antihypertensive, anti-tuberculosis, anticonvulsant, and antimicrobial, among others. QSAR and molecular docking studies were carried out on thirty-two (32) derivatives of 2,5-disubstituted-1,3,4-thiadiazoles for their antifungal activities toward Phytophthora infestans. Using the “graphical user interface” of Spartan14 software, the structure of the compounds of the dataset is drawn and then optimized at DFT/B3LYP/6-31G* quantum mechanical method of the software. Molecular descriptors of the optimized compounds were calculated and later on divided into the training set and test sets (at a ratio of 3:1). The training set was used for model generation and the test set was for external validation of the generated model. Four models were generated by the employment of genetic function approximation (GFA) in which the optimal model (4) turned out to have the following statistical parameters: R2 = 0.798318, R2adj = 0.750864, cross-validation R2(Q2cv) = 0.662654, and external validation R2pred = 0.624008. On the molecular docking study of thiadiazole compounds with the target protein of Phytophthora infestans effector site (PDB ID: 2NAR ), compound 13 shows the highest binding affinity with − 9.3 kcal/mol docking score and composes hydrophobic as well as H-bond interactions with the target protein (2NAR). The result of the QSAR study signifies the stability and robustness of the built model by considering the validation parameters and this gave an idea of template/ligand-based design while the molecular docking study revealed the binding interaction between the ligand and the protein site which gave an insight toward an “optimization method” of the structure-based design for the discovery of more potent compounds with better activity against Phytophthora infestans using the approach of computer-aided drug design (CADD) in plant pathology.

中文翻译：

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含有 5-苯基-2-呋喃的新型 2,5-分布-1,3,4-噻二唑衍生物作为抗致病疫霉的杀菌剂的 QSAR 和分子对接研究

1,3,4-噻二唑是被发现在制药和农用化学品领域最重要的结构部分，因为它们具有广泛的生物活性，包括抗肿瘤、抗菌、抗炎、抗高血压、抗抗结核药、抗惊厥药、抗菌药等。对 2,5-二取代-1,3,4-噻二唑的三十二 (32) 种衍生物进行了 QSAR 和分子对接研究，以确定它们对致病疫霉的抗真菌活性。使用Spartan14软件的“图形用户界面”，绘制数据集的化合物结构，然后在软件的DFT/B3LYP/6-31G*量子力学方法上进行优化。计算优化化合物的分子描述符，然后将其分为训练集和测试集（比例为 3：1）。训练集用于模型生成，测试集用于生成模型的外部验证。使用遗传函数逼近 (GFA) 生成了四个模型，其中最优模型 (4) 具有以下统计参数：R2 = 0.798318，R2adj = 0.750864，交叉验证 R2(Q2cv) = 0.662654，和外部验证 R2pred = 0.624008。在噻二唑化合物与致病疫霉效应位点靶蛋白（PDB ID：2NAR）的分子对接研究中，化合物 13 显示出最高的结合亲和力，具有 - 9.3 kcal/mol 对接评分，并与目标蛋白 (2NAR)。