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Abstract
QSAR studies were performed on a set of 43 analogs of thienopyrimidine using V-Life Molecular Design Suite (MDS 3.5) QSAR plus module by using Multiple Linear Regression (MLR) and Partial Least Squares (PLS) Regression methods against a gram positive (S.aureus) and a gram negative (E.coli) bacteria. MLR method has shown a very promising prediction results in both S.aureus and E.coli. QSAR model was generated by a training set of 34 molecules with correlation coefficient (r2) of 0.9849, 0.8719, significant cross validated correlation coefficient (q2) of 0.8881, 0.7811 and F test of  40.4301, 40.4768 respectively. In the selected descriptors, alignment independent descriptors such as T_C_C_7, T_N_O_3, T_2_N_1, T_N_O_1, T_O_O_7 and T_N_Cl_4 were the most important descriptors in predicting antibacterial activity.
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