SAR and QSAR in Environmental Research

Papers
(The TQCC of SAR and QSAR in Environmental Research is 6. 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 2020-05-01 to 2024-05-01.)
ArticleCitations
Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseas53
Chemometric methods in antimalarial drug design from 1,2,4,5-tetraoxanes analogues42
Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines27
Optimizing cardio, hepato and phospholipidosis toxicity of the Bedaquiline by chemoinformatics and molecular modelling approach24
Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling24
In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation23
QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation23
A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants23
Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation21
Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish20
Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures19
Novel molecular hybrid geometric-harmonic-Zagreb degree based descriptors and their efficacy in QSPR studies of polycyclic aromatic hydrocarbons18
Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses17
QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm17
Structure-based discovery of interleukin-33 inhibitors: a pharmacophore modelling, molecular docking, and molecular dynamics simulation approach16
The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors16
QSAR and molecular docking modelling of anti-leishmanial activities of organic selenium and tellurium compounds15
CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors14
Molecular mechanism concerning conformational changes of CDK2 mediated by binding of inhibitors using molecular dynamics simulations and principal component analysis14
Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent14
Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis14
Sample-size dependence of validation parameters in linear regression models and in QSAR12
QSAR modelling of organic dyes for their acute toxicity in Daphnia magna using 2D-descriptors12
SAR and QSAR research on tyrosinase inhibitors using machine learning methods12
Cross-validation strategies in QSPR modelling of chemical reactions11
Binding modes of GDP, GTP and GNP to NRAS deciphered by using Gaussian accelerated molecular dynamics simulations10
Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studi10
Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study9
Molecular modelling on SARS-CoV-2 papain-like protease: an integrated study with homology modelling, molecular docking, and molecular dynamics simulations9
Design of phosphoryl containing podands with Li+/Na+ selectivity using machine learning9
Insights into interaction mechanism of inhibitors E3T, E3H and E3B with CREB binding protein by using molecular dynamics simulations and MM-GBSA calculations9
Using in silico modelling and FRET-based assays in the discovery of novel FDA-approved drugs as inhibitors of MERS-CoV helicase9
Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis9
Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CLpro inhibitors: theoretical justification in light of experimental evidences9
Discovery of Zafirlukast as a novel SARS-CoV-2 helicase inhibitor using in silico modelling and a FRET-based assay8
Exploring RdRp–remdesivir interactions to screen RdRp inhibitors for the management of novel coronavirus 2019-nCoV8
Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: an attempt to identify promising structural features for potent HDAC2 inhibition8
Discovery of benzothiazole-based thiazolidinones as potential anti-inflammatory agents: anti-inflammatory activity, soybean lipoxygenase inhibition effect and molecular docking studies8
The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds8
Synthesis and molecular modelling of thiadizole based hydrazone derivatives as acetylcholinesterase and butyrylcholinesterase inhibitory activities8
Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach8
2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors7
iORI-ENST: identifying origin of replication sites based on elastic net and stacking learning7
Insights from computational studies on the potential of natural compounds as inhibitors against SARS-CoV-2 spike omicron variant7
Turning down PI3K/AKT/mTOR signalling pathway by natural products: an in silico multi-target approach7
In silico guided design of non-covalent inhibitors of DprE1: synthesis and biological evaluation7
Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression7
QSPR modelling for intrinsic viscosity in polymer–solvent combinations based on density functional theory7
Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential7
Synthesis of new benzimidazole derivatives containing 1,3,4-thiadiazole: their in vitro antimicrobial, in silico molecular docking and molecular dynamic simulations studies6
Decoding molecular mechanism underlying binding of drugs to HIV-1 protease with molecular dynamics simulations and MM-GBSA calculations6
Modelling quantitative structure activity–activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation6
QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis6
Investigating potency of TMC-126 against wild-type and mutant variants of HIV-1 protease: a molecular dynamics and free energy study6
Microwave-assisted organic synthesis, antimycobacterial activity, structure–activity relationship and molecular docking studies of some novel indole-oxadiazole hybrids6
A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors6
Thorough evaluation of OECD principles in modelling of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine derivatives using QSARINS6
The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method6
Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods6
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