Cilnidipine is a 1,4-dihydropyridine derived L/N-type calcium channel dual blocker possessing neuroprotective and analgesic effects which are related to its N-type calcium channel inhibitory activity. Calcium channel blockers are widely used for the treatment of various cardiac disorders. The existing calcium channel blockers have several short comings; hence there is a need to develop better drugs with better therapeutic profile. 2D-QSAR approach has been useful in such cases. A number of 1, 4- dihydropyridines like amlodipine are extensively used in therapy of cardiovascular disorders. Looking into importance of calcium channel blockers, a series of 1, 4- dihydropyridines was selected and different models based on Multiple linear regression (MLR), Principal component regression (PCR) and Partial Least Squares regression (PLR) analysis were generated to find out correlation between the physicochemical parameters and the biological activity. Multiple linear regression (MLR) coupled with stepwise variable selection led to a statistically significant model as compared to PLR and PCR with respect to r2 (coefficient of determination 0.9176) and q2 (cross- validation > 0.6673). Three descriptors are included in 2D- QSAR equation generated by using MLR.