SAR and QSAR in Environmental Research

Papers
(The H4-Index of SAR and QSAR in Environmental Research is 14. 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 2021-11-01 to 2025-11-01.)
ArticleCitations
Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm76
Identification of potential inhibitors of hypoxanthine-guanine phosphoribosyl transferase for cancer treatment by molecular docking, dynamics simulation and in vitro studies41
Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential32
Computational investigations of flavonoids as ALDH isoform inhibitors for treatment of cancer28
Discovery of dual-target natural antimalarial agents against DHODH and PMT of Plasmodium falciparum : pharmacophore modelling, molecular docking, quantum mechanics, and 23
Metrics for estimating vapour pressure deviation from ideality in binary mixtures17
In silico package models for deriving values of solute parameters in linear solvation energy relationships17
Two QSAR models for predicting the toxicity of chemicals towards Tetrahymena pyriformis based on topological-norm descriptors and spatial-norm descriptors16
Classification of ULK1 inhibitors and SAR analysis by machine learning methods16
Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase15
First report on q-RASTR modelling of hazardous dose (HD 5 ) for acute toxicity of pesticides: an efficient and reliable approach towards safeguarding the sensitive a15
Predicting repurposed drugs targeting the NS3 protease of dengue virus using machine learning-based QSAR, molecular docking, and molecular dynamics simulations15
Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey14
In silico insights into design of novel VEGFR-2 inhibitors: SMILES-based QSAR modelling, and docking studies on substituted benzo-fused heteronuclear derivatives14
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