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gaQSAR  

QSAR Modelling Using Genetic Algorithm Based Variable Selection
View on CRAN: Click here


Download and install gaQSAR package within the R console
Install from CRAN:
install.packages("gaQSAR")

Install from Github:
library("remotes")
install_github("cran/gaQSAR")

Install by package version:
library("remotes")
install_version("gaQSAR", "1.2.3")



Attach the package and use:
library("gaQSAR")
Maintained by
Jos Hageman
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-24
Latest Update: 2026-06-24
Description:
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>.
How to cite:
Jos Hageman (2026). gaQSAR: QSAR Modelling Using Genetic Algorithm Based Variable Selection. R package version 1.2.3, https://cran.r-project.org/web/packages/gaQSAR. Accessed 27 Aug. 2026.
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Full gaQSAR package functions and examples
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