QSAR Modelling Using Genetic Algorithm Based Variable Selection // Implements genetic algorithm-based variable selection for building quantitative structure-activity relationship (QSAR) models. The package provides a workflow for selecting optimal predictor subsets from large descriptor spaces using leave-one- out cross-validation (LOOCV) with Q2 as the fitness criterion. Features include automatic handling of multicollinearity via variance inflation factor (VIF) thresholding, customizable genetic algorithm operators, and diagnostic tools for model evaluation. Supports both training set optimization and external validation, plus nested (double) cross-validation for unbiased performance estimation and predictor stability diagnostics. Built-in visualization functions include Q2 curves and Williams plots to assess model applicability domain. The method is demonstrated in papers predicting antibacterial activity by Araya-Cloutier et al. (2018) doi:10.1038/s41598-018-27545-4 and Kalli et al. (2021) doi:10.1038/s41598-021-92964-9.