rQSAR: QSAR Modeling with Multiple Algorithms: MLR, PLS, and RandomForest
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 |
| DOI: | 10.32614/CRAN.package.rQSAR |
| Author: | Oche Ambrose George [aut, cre] |
| Maintainer: | Oche Ambrose George <ocheab1 at gmail.com> |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| CRAN checks: | rQSAR results |
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