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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 |
| DOI: |
10.32614/CRAN.package.rQSAR |
| Author: |
Oche Ambrose George
 [aut, cre] |
| Maintainer: |
Oche Ambrose George <ocheab1 at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| CRAN checks: |
rQSAR results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.