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MLR BestSubsetSelection 2.1 [Last Updated on 3rd June 2016] : To select best descriptor combination out of set of descriptors by evaluating all possible combinations of descriptors in the input file. Along with the conventional parameters like R2, Q2, Q2f1, Q2F2; the prediction quality of training as well as test set is judged using recently reported MAE-based criteria. Further using the MAE-based metrics, a QSAR score is computed that can be used to select the best QSAR models in terms of prediction quality. User can define r^2 cut-off and inter-correlation cut-off values, which is useful to reduce the computational time and to remove models with inter-correlated descriptors, respectively.
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 and test sets input files (.xlsx/.xls/.csv) before using the program (sample files are provided in Data Folder). *Manual is provided in the program folder.
File Format: Compound number (first column), Descriptors (Subsequent Columns), Activity/Property (Last column)