To Download: Click Here

Genetic Algorithm v4.1 (GA; Last Updated on 24th March 2017) is a search heuristic method that mimics the process of natural selection. Where the exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, crossover, mutation, and selection. Here, the Genetic Algorithm tool performs the genetic algorithm for selection of significant variables (descriptors) during QSAR model development using Fitness Function based on recently reported MAE-based criteria. Note that in version 4.1, you can perform process validation. 


To Download and Run the Program

Click on the download link above (it will direct you to google drive) and then press "ctrl + S (Windows) or cmd+S (Macs)" to save as zip file. Extract the .zip file and click on .jar file to run the program.

Note: The program folder will consist of three folders "Data", "Lib" and "Output". For user convenience, user may keep input files in "Data" folder and may save output file in "Output" folder."Lib" folder consist of library files required for running the program. Check the format of training set input file (.xlsx/.xls/.csv) before using the program (sample file is provided in Data Folder). *Manual is provided in the program folder.

File Format: Compound number (first column), Descriptors (Subsequent Columns), Activity/Property (Last column)

                                          Reference Articles for Genetic Algorithm Tool

  • Ambure, P., Aher, R. B., Gajewicz, A., Puzyn, T., & Roy, K. (2015). “NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling. Chemometrics and Intelligent Laboratory Systems, 147, 15 October 2015, Pages 1–13. doi:10.1016/j.chemolab.2015.07.007

  • Ambure P, Roy K, Understanding Structural Requirements of Cyclic Sulfone Hydroxyethylamines as hBACE1 Inhibitors against Aβ Plaques in Alzheimer’s Disease: A Predictive QSAR Approach. RSC Advances, 6, 2016, 28171-28186, http://dx.doi.org/10.1039/C6RA04104C