DiPCell is a webserver for the predicting inhibitory activity of unknown molecules and designing their analogs against pancreatic cancer cell lines. DiPCell implements the QSAR models, which were developed by using SMOreg machine learning algorithm on high throughput drug screening data. This high throughput screening data is obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database.

DiPCell takes the SDF of an unknown molecule as input and predicts the inhibitory activity in terms of IC50 value. One of the major feature of DiPCell is that it generates the analogs of molecule and subsequent predicts of inhibitory activity of all the analogs. By this feature, user can select the best analog of any molecule as a inhibitory molecule.

Experimental procedure to screen unknown molecule library for some inhibitory activity is laborious and time consuming procedure, which is one of the bottleneck in drug development process. DiPCell can help in the computational screening of compound libraries to circumvent the search of drug candidates for pancreatic cancer.