QSAR Modeling with Multiple Algorithms: MLR, PLS, and Random Forest
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
Show 4 earlier changesHide 4 earlier changes
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-04-06
- ERROR13 OK, 1 ERROR2026-04-04
- OK14 OK2026-03-10
Documentation
- Help pages
- 5
- Examples that run
- no examples
- Exported functions with examples
- 0%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
- QSAR Workflow HTML
Downloads
Dependencies
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.0.0Latest2024-04-02 · current release
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2024-04-02
- Total releases
- 1 / 2 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.6.0
- Download size
- 208 KB
Cite
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