A genetic programming-based approach to identify potential inhibitors of serine protease of .

We applied genetic programming approaches to understand the impact of descriptors on inhibitory effects of serine protease inhibitors of () and the discovery of new inhibitors as drug candidates. The experimental dataset of serine protease inhibitors of descriptors was optimized by genetic algorithm (GA) along with the correlation-based feature selection (CFS) in order to develop predictive models using machine-learning algorithms. The best model was deployed on a library of 918 phytochemical compounds to screen potential serine protease inhibitors of . Quality and performance of the predictive models were evaluated using various standard statistical parameters.  The best random forest model with CFS-GA screened 126 anti-tubercular agents out of 918 phytochemical compounds. Also, genetic programing symbolic classification method is optimized descriptors and developed an equation for mathematical models. The use of CFS-GA with random forest-enhanced classification accuracy and predicted new serine protease inhibitors of , which can be used for better drug development against tuberculosis.