gaQSAR: 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>.

Version:	1.2.3
Imports:	GA, future, future.apply, ggplot2, ggrepel, stats, scales, prospectr, reshape2
Suggests:	knitr, rmarkdown, QSARdata
Published:	2026-06-24
DOI:	10.32614/CRAN.package.gaQSAR (may not be active yet)
Author:	Jos Hageman [aut, cre]
Maintainer:	Jos Hageman <jos.hageman at wur.nl>
BugReports:	https://github.com/joshageman/gaQSAR/issues
License:	GPL-3
URL:	https://github.com/joshageman/gaQSAR
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	gaQSAR results

Documentation:

Reference manual:	gaQSAR.html , gaQSAR.pdf
Vignettes:	Double cross-validation workflow with gaQSAR (source, R code) Train/test QSAR workflow with gaQSAR (source, R code)

Downloads:

Package source:	gaQSAR_1.2.3.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): gaQSAR_1.2.3.tgz, r-oldrel (arm64): gaQSAR_1.2.3.tgz, r-release (x86_64): gaQSAR_1.2.3.tgz, r-oldrel (x86_64): gaQSAR_1.2.3.tgz

Linking:

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