QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants

Abstract

Ionic liquids (ILs) have been popular in many industrial and chemical processes, like antimicrobial properties, solvents, and synthesis of new compounds with antioxidant activity. Because of the significance of their application, the prediction minimal inhibitory concentration (MIC) of 204 ILs and the minimal bactericidal concentration (MBC) of 114 ILs of them against Staphylococcus aureus (S. aureus) have been carried out using the quantitative structure activity relationship (QSAR) based on the Monte Carlo method. Using the simplified molecular input line entry system (SMILES) notation, molecular structures of all of ILs were displayed. Hybrid optimal descriptor was employed in developing the model for pMIC and pMBC, which was obtained by combining the molecular graph and SMILES. For pMIC, hybrid optimal descriptors were calculated via SMILES and hydrogen-suppressed molecular graph (HSG), as well as hybrid optimal descriptors for pMBC were calculated via SMILES and hydrogen-filled graph (HFG). The total dataset was randomly split into training, invisible training, calibration, and validation set for three times. Statistically analyzed by the calculated descriptors, a QSAR model was developed for pMIC and pMBC of ILs, and the index of ideality of correlation (IIC) was examined as a benchmark for predictive potential of these models. Their correlation coefficient (R2) values of the training, invisible training, calibration, and validation sets for three splits were 0.8585–0.8853, 0.8523–0.8898, 0.8809–0.9240, and 0.8036–0.8903 for pMIC and 0.8357–0.8991, 0.8223–0.9306, 0.8372–0.9170, and 0.8171–0.8901 for pMBC, respectively. The results show that the predictability to develop the QSAR model for all splits is at a high level.