QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method.
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Summary
A series of 107 1-hydroxyethoxy)-methyl]-6-(phenylthio) thymine with anti-HIV-1 activity as a non-nucleoside reverse transcriptase inhibitor (NNRTI) has been studied and defined structural alerts for increase and decrease of the IC50 are defined.
- Type
- article
- Published
- 2014-04-22
- Cited by
- 66
- References
- 50
- OpenAlex
- https://openalex.org/W24657566
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:34831099
Keywords
Question answering, Computer science, Natural language, Natural language processing, Natural (archaeology)
References
- QSAR MODELLING OF ANTI-HIV ACTIVITY WITH HEPT DERIVATIVES
- Molecular descriptors in QSAR/QSPR
- Handbook of Molecular Descriptors
- A structure–activity relationship study of HEPT-analog compounds with anti-HIV activity
- CORAL: building up the model for bioconcentration factor and defining it's applicability domain.
- Further exploring rm2 metrics for validation of QSPR models
- QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES
- A 3D QSAR study of a series of HEPT analogues: the influence of conformational mobility on HIV-1 reverse transcriptase inhibition.
- Exploring the impact of size of training sets for the development of predictive QSAR models
- QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network
- Structure-activity relationships of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine analogues: effect of substitutions at the C-6 phenyl ring and at the C-5 position on anti-HIV-1 activity.
- Non-nucleoside inhibitors of HIV-1 reverse transcriptase: molecular modeling and X-ray structure investigations.
- New QSPR study for the prediction of aqueous solubility of drug-like compounds.
- Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: Using rare SMILES attributes to define the applicability domain.
- Use of Artificial Neural Networks in a QSAR Study of Anti-HIV Activity for a Large Group of HEPT Derivatives
- HIV-reverse transcriptase inhibition: inclusion of ligand-induced fit by cross-docking studies.
- Evolutionary optimization, backpropagation, and data preparation issues in QSAR modeling of HIV inhibition by HEPT derivatives.
- A new class of HIV-1-specific 6-substituted acyclouridine derivatives: synthesis and anti-HIV-1 activity of 5- or 6-substituted analogues of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT).
- SMILES‐Based QSAR Models for the Calcium Channel‐Antagonistic Effect of 1,4‐Dihydropyridines
- QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES.
Cited by
- Exploration of good and bad structural fingerprints for inhibition of indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modeling
- Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors
- Monte Carlo Method‐Based QSAR Modeling of Penicillins Binding to Human Serum Proteins
- QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors
- Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase
- QSAR model as a random event: A case of rat toxicity.
- Comparative validated molecular modeling of p53-HDM2 inhibitors as antiproliferative agents.
- Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors
- Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO2 nanoparticles.
- Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors
- Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors
- In Silico Drug-Designing Studies on Flavanoids as Anticolon Cancer Agents: Pharmacophore Mapping, Molecular Docking, and Monte Carlo Method-Based QSAR Modeling
- Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis
- Modification of polychlorinated phenols and evaluation of their toxicity, biodegradation and bioconcentration using three-dimensional quantitative structure-activity relationship models.
- Predictive Quantitative Structure Toxicity Relationship Study on Avian Toxicity of Some Diverse Agrochemical Pesticides by Monte Carlo Method: QSTR on Pesticides
- Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors
- QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method
- Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors
- Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data.
- Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques
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