BackgroundThe 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.ResultsQSAR 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).ConclusionThe 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.