Machine learning-based QSAR modeling assay for the nitrification inhibition of 2,4,5-trichloroaniline-derived eco-friendly Schiff bases

RSC Advances 2026 Open access

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Authors: Tilak Mondal, Rajesh Kumar, Santanu Mukherjee, Nilimesh Mridha, Namrata Laskar, Avijit Das, Pradip Basak, Atul Singha, Kanchan Sinha, Dinesh Babu Shakyawar
DOI: 10.1039/d5ra09828a
Cited by: 1
References: 36

Abstract

-test was performed within the treatment concentration and found that all the treatments differed significantly in terms of mean responses from each other. Statistical and machine leaning models such as stepwise multiple linear regression, artificial neural network (ANN), support vector regression and random forest were used to validate the quantitative structure activity relationship (QSAR) of the synthesized compounds with their nitrification inhibition property. ANN models developed based on feature variables selected from random forest regression were found to be the best fitted models for predicting nitrification inhibition activity at 1%, 5% and 10% of treatment doses.

Fields of Study & Subjects

Computational Drug Discovery Methods Peroxisome Proliferator-Activated Receptors Marine Biology and Environmental Chemistry

Publication details

Journal / source
RSC Advances
Publisher
Royal Society of Chemistry (RSC)
Volume
16
Issue
12
Pages
10549–10564
Publication date
1 January 2026
Language
EN