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 2021-04-01 to 2025-04-01.)
ArticleCitations
Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents52
Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach31
Targeting human arginyltransferase and post-translational protein arginylation: a pharmacophore-based multilayer screening and molecular dynamics approach to discover novel inhibitors with therapeutic26
Structure-based drug design of pre-clinical candidate nanopiperine: a direct target for CYP1A1 protein to mitigate hyperglycaemia and associated microbes24
Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods22
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 studies17
Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm16
Computational studies with flavonoids and terpenoids as BRPF1 inhibitors: in silico biological activity prediction, molecular docking, molecular dynamics simulations, MM/PBSA calculations15
QSAR analysis of sodium glucose co–transporter 2 (SGLT2) inhibitors for anti-hyperglycaemic lead development13
QSAR analysis and experimental evaluation of new quinazoline-containing hydroxamic acids as histone deacetylase 6 inhibitors13
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
Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids12
The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds12
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