Literature DB >> 25520208

QSAR as a random event: a case of NOAEL.

Alla P Toropova1, Andrey A Toropov, Jovana B Veselinović, Aleksandar M Veselinović.   

Abstract

Quantitative structure-activity relationships (QSAR) for no observed adverse effect levels (NOAEL, mmol/kg/day, in logarithmic units) are suggested. Simplified molecular input line entry systems (SMILES) were used for molecular structure representation. Monte Carlo method was used for one-variable models building up for three different splits into the "visible" training set and "invisible" validation. The statistical quality of the models for three random splits are the following: split 1 n = 180, r (2) = 0.718, q (2) = 0.712, s = 0.403, F = 454 (training set); n = 17, r (2) = 0.544, s = 0.367 (calibration set); n = 21, r (2) = 0.61, s = 0.44, r m (2) = 0.61 (validation set); split 2 n = 169, r (2) = 0.711, q (2) = 0.705, s = 0.409, F = 411 (training set); n = 27, r (2) = 0.512, s = 0.461 (calibration set); n = 22, r (2) = 0.669, s = 0.360, r m (2) = 0.63 (validation set); split 3 n = 172, r (2) = 0.679, q (2) = 0.672, s = 0.420, F = 360 (training set); n = 19, r (2) = 0.617, s = 0.582 (calibration set); n = 21, r (2) = 0.627, s = 0.367, r m (2) = 0.54 (validation set). All models are built according to OCED principles.

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Year:  2014        PMID: 25520208     DOI: 10.1007/s11356-014-3977-2

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  24 in total

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5.  QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

Authors:  Andrey A Toropov; Alla P Toropova; Tomasz Puzyn; Emilio Benfenati; Giuseppina Gini; Danuta Leszczynska; Jerzy Leszczynski
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6.  Subchronic Oral and Inhalation Toxicities: a Challenging Attempt for Modeling and Prediction.

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8.  A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

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9.  QSAR study and molecular design of open-chain enaminones as anticonvulsant agents.

Authors:  Juan C Garro Martinez; Pablo R Duchowicz; Mario R Estrada; Graciela N Zamarbide; Eduardo A Castro
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10.  A New Way in Deciding NOAEL Based on the Findings from GLP-Toxicity Test.

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2.  Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels.

Authors:  Ly Ly Pham; Sean Watford; Prachi Pradeep; Matthew T Martin; Russell Thomas; Richard Judson; R Woodrow Setzer; Katie Paul Friedman
Journal:  Comput Toxicol       Date:  2020-08-01

3.  In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs.

Authors:  Fabiola Pizzo; Domenico Gadaleta; Emilio Benfenati
Journal:  Methods Mol Biol       Date:  2022

4.  Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.

Authors:  Apilak Worachartcheewan; Virapong Prachayasittikul; Alla P Toropova; Andrey A Toropov; Chanin Nantasenamat
Journal:  Mol Divers       Date:  2015-11       Impact factor: 2.943

5.  CORAL: model for no observed adverse effect level (NOAEL).

Authors:  Andrey A Toropov; Alla P Toropova; Fabiola Pizzo; Anna Lombardo; Domenico Gadaleta; Emilio Benfenati
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6.  How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases.

Authors:  Natalja Fjodorova; Marjana Novič; Katja Venko; Viktor Drgan; Bakhtiyor Rasulev; Melek Türker Saçan; Safiye Sağ Erdem; Gulcin Tugcu; Alla P Toropova; Andrey A Toropov
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7.  Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

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Review 8.  QSPR/QSAR: State-of-Art, Weirdness, the Future.

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  8 in total

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