Pancreatic cancer remains the most devastating disease with worst prognosis. There is a pressing need to accelerate the drug discovery process to identify new effective drug candidates against pancreatic cancer. We have developed QSAR models for predicting promiscuous inhibitors using the pharmacological data. Our models achieved maximum Pearson correlation coefficient of 0.86, when evaluated on 10-fold cross-validation. Our models have also successfully validated the drug-to-oncogene relationship and further we used these models to screen FDA approved drugs and tested them in vitro. We have integrated these models in a webserver named as DiPCell, which will be useful for screening and designing novel promiscuous drug molecules. We have also identified the most and least effective drugs for pancreatic cancer cell lines. On the other side, we have identified resistant pancreatic cancer cell lines, which need investigative scanner on them to put light on resistant mechanism in pancreatic cancer.