rQSAR: QSAR Modeling with Multiple Algorithms: MLR, PLS, and Random Forest

Quantitative Structure-Activity Relationship (QSAR) modeling is a valuable tool in computational chemistry and drug design, where it aims to predict the activity or property of chemical compounds based on their molecular structure. In this vignette, we present the 'rQSAR' package, which provides functions for variable selection and QSAR modeling using Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Random Forest algorithms.

Version: 1.0.0
Depends: R (≥ 3.6.0), dplyr, corrplot, tibble, gridExtra
Imports: utils, rcdk (≥ 3.8.1), ggplot2, caret, pls, randomForest, leaps, stats
Suggests: rmarkdown, knitr
Published: 2024-04-02
Author: Oche Ambrose George ORCID iD [aut, cre]
Maintainer: Oche Ambrose George <ocheab1 at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: rQSAR results


Reference manual: rQSAR.pdf
Vignettes: QSAR Workflow


Package source: rQSAR_1.0.0.tar.gz
Windows binaries: r-prerel: rQSAR_1.0.0.zip, r-release: rQSAR_1.0.0.zip, r-oldrel: rQSAR_1.0.0.zip
macOS binaries: r-prerel (arm64): rQSAR_1.0.0.tgz, r-release (arm64): rQSAR_1.0.0.tgz, r-oldrel (arm64): rQSAR_1.0.0.tgz, r-prerel (x86_64): rQSAR_1.0.0.tgz, r-release (x86_64): rQSAR_1.0.0.tgz


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