rasar 0.1.0

The components of this module can be used for read-across related calculations.

#rasar The components of this module can be used for read-across related calculations. It is a crucial module for cheminformatics applications.

Installation

pip install rasar

Usage

This module supports five different read-across tasks, including pairwise similarity calculation, read-across prediction, read-across optimization, read-across feature importance calculation, and rasar descriptor calculation.

Getting started

import pandas as pd from rasar import ra_similarity, ra_pred, ra_optimization, ra_importance, calculate_descriptor tr = pd.read_excel('train.xlsx', index_col=0) te = pd.read_excel('test.xlsx', index_col=0) xtr = tr.iloc[:,:-1] ytr = tr.iloc[:,-1] xte = te.iloc[:,:-1] yte = te.iloc[:,-1] sim = ra_similarity(des_tr=xtr, des_te=xte) sim1 = sim.similarity_calculation(method='Euclidean Distance') pred = ra_pred(df1=tr, df2=te).weighted_prediction(method='Laplacian Kernel', ctc=6, gamma=0.5) opt = ra_optimization(method='Laplacian Kernel', data=tr, parameters={'CTC': [1, 3, 6, 10], 'Gamma': [0.1, 0.5, 1], 'Threshold': [0.0]}, objective_function="MAE", cv_fold=5) imp = ra_importance(df1=tr).imp_calculation(method='Laplacian Kernel', ctc=6, gamma=0.5, ths=2) ra_importance(df1=tr).plot_importance(imp_df=imp, plot_type='coefficient', color="winter_r", index=1) des_tr, des_te = calculate_descriptor(df1=tr, df2=te, method='Laplacian Kernel', ctc=6, gamma=0.5, merge = True)

##Cite To use this module, users need to cite the following paper:

Pore, S. and Roy, K., 2025. “intelligent Read Across (iRA)”-A tool for read-across-based toxicity prediction of nanoparticles. Computational and Structural Biotechnology Journal. https://doi.org/10.1016/j.csbj.2025.07.032

##LICENSE Apache License 2.0