Multiple Linear Regression and Principal Component Analysis‐Based Prediction of the Anti‐Tuberculosis Activity of Some 2‐aryl‐1,3,4‐Thiadiazole Derivatives

Abstract

AbstractThe search for novel antibacterial agents active against Mycobacterium tuberculosis and other atypical Mycobacterium is urgent due to the lack of effectiveness of known anti‐tuberculosis agents against opportunistic pathogens as a consequence of rapidly emerging resistance. In this study, QSAR analysis was conducted on the anti‐tuberculosis activity of some newly synthesized 2‐aryl‐1,3,4‐Thiadiazole derivatives. Different set of molecular descriptors were calculated to predict the anti‐tuberculosis activity of a set of 27 thiadiazole derivatives using the multiple linear regression (MLR), Free‐Wilson analysis (FWA) and principal component analysis (PCA) methods. Successful MLR equations were obtained from a pool of topological, constitutional, quantum chemical and entire set of descriptors. They could predict the activity of all of the molecules, except one derivative, accurately. The equations obtained from the functional groups and chemical descriptors had lower accuracy and produced two and three outliers, respectively. In the PCA, different patterns of distributions were obtained by plotting the first three scores of the descriptors data matrices against each other. Better classification patterns were obtained when PCA was performed on the subset of descriptors in relative to all of the calculated descriptors. In this case, the active and inactive molecules were classified into distinct clusters with exception one active molecules, which classified as inactive.