Colorectal Cancer has an incidence rate of 4.9% worldwide, as per the GLOBOCAN 2020 report.
Mortality rate of colorectal cancer patients is 4.5%.
The 5-year prevalence rate of the colorectal cancer is 10.19.
ColoRecPred is a webserver developed to predict the drug activity of unknown molecules or compounds. The prediction server has been developed using AI/ML QSAR models. The webserver can be utilised by the researchers all around the world to evaluate the drug activity on the specific cell lines of colorectal cancer.
This tool shall be useful in predicting the effective drugs for colorectal cancer from the arsenal of the available drugs worldwide to aid in drug repurposing or drug repositioning.
To build the models for the different cell lines, we used the drug activity dataset from GDSC website (https://www.cancerrxgene.org/) and simultaneously extracted the descriptors from the PaDEL software. Moreover this was subjected to feature selection and then these input data were feeded into Support Vector Machine algorithm for training and testing. After evaluating the performances, the models were implemented and hosted on this webserver.