SAR and QSAR in Environmental Research

Papers
(The H4-Index of SAR and QSAR in Environmental Research is 12. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-02-01 to 2025-02-01.)
ArticleCitations
Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents40
Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach29
Classification models and SAR analysis on thromboxane A2 synthase inhibitors by machine learning methods24
Structure-based drug design of pre-clinical candidate nanopiperine: a direct target for CYP1A1 protein to mitigate hyperglycaemia and associated microbes22
Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods17
A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors17
Discovery of dual-target natural inhibitors of meprins α and β metalloproteases for inflammation regulation: pharmacophore modelling, molecular docking, ADME prediction, and molecular dynamics studies15
Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm13
QSAR analysis and experimental evaluation of new quinazoline-containing hydroxamic acids as histone deacetylase 6 inhibitors13
Computational studies with flavonoids and terpenoids as BRPF1 inhibitors: in silico biological activity prediction, molecular docking, molecular dynamics simulations, MM/PBSA calculations13
QSAR analysis of sodium glucose co–transporter 2 (SGLT2) inhibitors for anti-hyperglycaemic lead development12
Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids12
Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential12
Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models12
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