4.3.1 Introduction Antimicrobials are drugs containing antifungal antibiotic, antiallergic, antivirus etc. properties. Misuse, overuse of these drugs and by the condition of environment microorganisms are becoming resistant resulting a world health problem. It is associated with an increase in mortality1. Therefore there is an essential need for new potent antimicrobial drugs treat life threatening diseases. There are a lot of work have been done, in recent years on heterocyclic organic compounds. A lot of azoles derivatives are synthesised like indole, 1,2,4-triazole, thiazole, imidazole, azetidinone etc.
1,2,4-triazoles derivative are well known in drug chemistry due to their many biological properties like antibiotic, antifungal2-8, antitubercular9-10, anticancer11, anti-tumar12, anti-inflammatory13-15, anticonvulsant16-17, urease inhibitor18 and antiviral activities19.
Quantitative structure-activity relationships (QSAR)/QSPR represent an attempt to correlate structural or property descriptors of given sets of compounds with activities. These physicochemical descriptors, which include topological, connectivity, eta etc. parameters to account for hydrophobicity, topology, molecular properties, and steric effects, are determined empirically or, more recently, by computational, statistical correlation regression methods.
Activities used in QSAR include chemical,medicinal and biological assays. QSAR currently are being applied in many disciplines, with many pertaining to drug design and environmental risk assessment. This method is based on data collection, molecular descriptors selection, correlation model evaluation. It gives idea about modeling of new potent antimicrobial azoles derivatives with predictive ability and mechanism of drug receptor interactions.
4.3.2 Inhibitory Activity
In the present study inhibitory activity of ethanoylamino,mercapto, phenyl-1,2,4-triazoles is used as a dependent variable in the form of MIC taken as C.
albicans 70µg/ml, which gives highest R2 value out of different antifungfal and antibacterial activity sets directly taken from the work of Upamanyu et. al20.
4.3.3 Presentation of Data
In this study, Table-4.3.1 represents the structure of studied compounds with inhibitory activity while Table-4.3.2 representrs the topological descriptor; BLI. Molicular indix Ui, MLOGP.Connectivity indix: X1A, X5V, X1AV and eta indix: Eta_betaP_A. Table-4.3.3 shows the correlation matrix among the descriptors which are used in this study. Table-4.3.4 is the cross validation statistical parameters. Table-4.3.5 shows regression statistical analysis and quality of correlation. Table-4.3.6 Antimicrobial screening i.e.the comparison between the observed and predicted activities of compounds for training sets with residual summary of ethanoylamino,mercapto, phenyl-1,2,4-triazoles. Fig-4.3.1 shows the graph plotted between the observed and calculated inhibitory activity. Fig-4.3.2 is the graph plotted between the observed and residual to illustrate the systemic error and Table 4.3.3 represent the graph plotted with ridge regression to show the multicollinearity.
Table 4.3.1: The structures of compounds studied and their antimicrobial activity
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