PIC50PRED: A PIC50 PREDICTION TOOL FOR 5-ALPHA REDUCTASE ENZYME - Balekundri, Urvashi; Madagi, Shivakumar (Asian Journal of Pharmaceutical and Clinical Research, 2016)
ABSTRACT Objectives: Prostate cancer is a major health burden all over the world. 5-alpha reductase (5AR) enzyme is a significant drug target for prostate cancer. Identification of drug targets and their inhibitors are a challenging task in drug designing. The prediction of potential inhibitors against 5AR may help in designing effective drugs against prostate cancer. Methods: The compounds having proven inhibitory action against 5AR in experimental settings were trained and tested to build two-dimensional quantitative structure-activity relationship (2D QSAR) models based on molecular descriptors. The molecular descriptors were extracted from E-Dragon 1.0 program. The 2D QSAR prediction models were built using linear regression and least median of squares using Weka 3.7. The optimized 2D QSAR models were implemented in a web-based server by employing XAMPP package and using hyper processed language (PHP) as a scripting language. Results: The 2D QSAR models were built using molecular descriptors and achieved a positive correlation of 0.69 (r) and 0.46 (r) between predicted and actual pIC50 from linear regression and least square of median, respectively. Conclusion: In silico QSAR modeling along with machine learning techniques seems to be a promising approach for prediction of novel 5AR inhibitors. To serve the scientific community, a web server pIC50 Predâ€ has been developed which allows the prediction of pIC50 value of any novel compounds thought to have 5AR inhibitory activity before jumping into in vitro experimental assays. Availability: The prediction tool is freely available at http://www.biopred.org. Keywords: 5-alpha reductase, Two-dimensional quantitative structure-activity relationship, pIC50, Weka, Linear regression, Least median of squares.