Parallelized Population Based Exhaustive Replacement Method for Reproducible Features Selection and Its Applications in Drug Discovery

We present herein, the parallelized Population based Exhaustive Replacement Method (PERM) and its applications for the reproducible selection of chemical features with improved exploration of search space. The improved performance of parallelized PERM over the earlier reports can be attributed to its ability to a) minimize local optimization and randomness of the method and b) effective exploration of search space. Consequently, it functions like a deterministic method with enhanced reproducibility and reliability. The application of parallelized PERM for the generation of reproducible predictive models is demonstrated using Human Serum Albumin and EGFR Kinase inhibitors data sets.