OOIR: Observatory of International 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.)

Article	Citations
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 diseas	53
Chemometric methods in antimalarial drug design from 1,2,4,5-tetraoxanes analogues	42
Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines	27
Optimizing cardio, hepato and phospholipidosis toxicity of the Bedaquiline by chemoinformatics and molecular modelling approach	24
Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling	24
A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants	23
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 correlation	23
QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation	23
Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation	21
Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish	20
Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures	19
Novel molecular hybrid geometric-harmonic-Zagreb degree based descriptors and their efficacy in QSPR studies of polycyclic aromatic hydrocarbons	18
QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm	17
Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses	17
The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors	16
Structure-based discovery of interleukin-33 inhibitors: a pharmacophore modelling, molecular docking, and molecular dynamics simulation approach	16
QSAR and molecular docking modelling of anti-leishmanial activities of organic selenium and tellurium compounds	15
Molecular mechanism concerning conformational changes of CDK2 mediated by binding of inhibitors using molecular dynamics simulations and principal component analysis	14
Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent	14
Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis	14
CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors	14
QSAR modelling of organic dyes for their acute toxicity in Daphnia magna using 2D-descriptors	12
SAR and QSAR research on tyrosinase inhibitors using machine learning methods	12
Sample-size dependence of validation parameters in linear regression models and in QSAR	12
Cross-validation strategies in QSPR modelling of chemical reactions	11

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 studi	10
Binding modes of GDP, GTP and GNP to NRAS deciphered by using Gaussian accelerated molecular dynamics simulations	10
Design of phosphoryl containing podands with Li⁺/Na⁺ selectivity using machine learning	9
Insights into interaction mechanism of inhibitors E3T, E3H and E3B with CREB binding protein by using molecular dynamics simulations and MM-GBSA calculations	9
Using in silico modelling and FRET-based assays in the discovery of novel FDA-approved drugs as inhibitors of MERS-CoV helicase	9
Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis	9
Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CL^pro inhibitors: theoretical justification in light of experimental evidences	9
Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study	9
Molecular modelling on SARS-CoV-2 papain-like protease: an integrated study with homology modelling, molecular docking, and molecular dynamics simulations	9
The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds	8
Synthesis and molecular modelling of thiadizole based hydrazone derivatives as acetylcholinesterase and butyrylcholinesterase inhibitory activities	8
Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach	8
Discovery of Zafirlukast as a novel SARS-CoV-2 helicase inhibitor using in silico modelling and a FRET-based assay	8
Exploring RdRp–remdesivir interactions to screen RdRp inhibitors for the management of novel coronavirus 2019-nCoV	8
Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: an attempt to identify promising structural features for potent HDAC2 inhibition	8
Discovery of benzothiazole-based thiazolidinones as potential anti-inflammatory agents: anti-inflammatory activity, soybean lipoxygenase inhibition effect and molecular docking studies	8
Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression	7
QSPR modelling for intrinsic viscosity in polymer–solvent combinations based on density functional theory	7
Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential	7
2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors	7
iORI-ENST: identifying origin of replication sites based on elastic net and stacking learning	7
Insights from computational studies on the potential of natural compounds as inhibitors against SARS-CoV-2 spike omicron variant	7
Turning down PI3K/AKT/mTOR signalling pathway by natural products: an in silico multi-target approach	7
In silico guided design of non-covalent inhibitors of DprE1: synthesis and biological evaluation	7
QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis	6
Investigating potency of TMC-126 against wild-type and mutant variants of HIV-1 protease: a molecular dynamics and free energy study	6
Microwave-assisted organic synthesis, antimycobacterial activity, structure–activity relationship and molecular docking studies of some novel indole-oxadiazole hybrids	6
A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors	6
Thorough evaluation of OECD principles in modelling of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine derivatives using QSARINS	6
The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method	6
Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods	6
Synthesis of new benzimidazole derivatives containing 1,3,4-thiadiazole: their in vitro antimicrobial, in silico molecular docking and molecular dynamic simulations studies	6
Decoding molecular mechanism underlying binding of drugs to HIV-1 protease with molecular dynamics simulations and MM-GBSA calculations	6
Modelling quantitative structure activity–activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation	6