R-universe search: qsar

Showing 7 of total 7 results (show query)

dosorio

Peptides:Calculate Indices and Theoretical Physicochemical Properties of Protein Sequences

Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' output files from the 'GROMACS' molecular dynamics package.

Maintained by Daniel Osorio. Last updated 1 years ago.

bioinformatics calculate-indices peptides protein-sequences qsar cpp

11.0 match 82 stars 9.14 score 245 scripts 7 dependents

tidymodels

applicable:A Compilation of Applicability Domain Methods

A modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See Netzeva et al (2005) <doi:10.1177/026119290503300209> for an overview of applicability domains.

Maintained by Marly Gotti. Last updated 2 years ago.

6.0 match 47 stars 7.42 score 47 scripts 1 dependents

ocheab

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.

Maintained by Oche Ambrose George. Last updated 12 months ago.

openjdk

12.4 match 2.00 score

r-forge

QSARdata:Quantitative Structure Activity Relationship (QSAR) Data Sets

Molecular descriptors and outcomes for several public domain data sets

Maintained by Max Kuhn. Last updated 14 years ago.

2.9 match 2.83 score 68 scripts

20k-p

DEMOVA:DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated using Test Set

Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.

Maintained by Vinca Prana. Last updated 9 years ago.

4.9 match 1.30 score 10 scripts

jrcodina

peptoolkit:A Toolkit for Using Peptide Sequences in Machine Learning

This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) <doi:10.32614/RJ-2015-001>). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning.

Maintained by Josep-Ramon Codina. Last updated 1 years ago.

2.3 match 2.70 score

lhnctr

Dforest:Decision Forest

Provides R-implementation of Decision forest algorithm, which combines the predictions of multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel pattern-recognition method which can be used to analyze: (1) DNA microarray data; (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and (3) Structure-Activity Relation (SAR) data. In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV. run Dforest() to see more instructions. Weida Tong (2003) <doi:10.1021/ci020058s>.

Maintained by Leihong Wu. Last updated 7 years ago.

3.6 match 1.00 score 8 scripts