QSAR and molecular docking studies of novel 2,5-distributed-1,3,4-thiadiazole derivatives containing 5-phenyl-2-furan as fungicides against Phythophthora infestans

Abstract

Background: 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 antitumor, antibacterial, anti-inflammatory, antihypertensive, anti-tuberculosis, anticonvulsant, and antimicrobial, among others. Results: 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: R 2 = 0.798318, R 2 adj = 0.750864, cross-validation R 2 (Q 2 cv) = 0.662654, and external validation R 2 pred = 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). Conclusion: 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.

Application of SSIR Method for the Design of Fungicides

“…Therefore, the active search for new active agents to counteract these pests is needed [8]. For this search, the aid of computational methods, such as QSAR [9,10], is increasingly being used in the development of new derivatives of known fungicides [11], in the design of new libraries for computational screening [12], in the identification of new lead structures [13] and in the screening of natural products [14].…”