Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results.

Quantitative structure-activity relationships (QSARs) for prediction of toxicological endpoints built up with the CORAL software are discussed. Prejudices related to these QSAR models are listed. Possible ways to improve the software are discussed.

[1]  Carmela Parenti,et al.  Sigma-2 receptor ligands QSAR model dataset , 2017, Data in brief.

[2]  I. Macdougall,et al.  The available intravenous iron formulations: History, efficacy, and toxicology , 2017, Hemodialysis international. International Symposium on Home Hemodialysis.

[3]  Emilio Benfenati,et al.  In silico methods to predict drug toxicity. , 2013, Current opinion in pharmacology.

[4]  Nigel Greene,et al.  Comparing Measures of Promiscuity and Exploring Their Relationship to Toxicity , 2012, Molecular informatics.

[5]  Andrey A Toropov,et al.  SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT(1A) receptor ligands using CORAL. , 2013, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[6]  Emilio Benfenati,et al.  Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: an unexpected good prediction based on a model that seems untrustworthy , 2010 .

[7]  E Benfenati,et al.  CORAL: building up the model for bioconcentration factor and defining it's applicability domain. , 2011, European journal of medicinal chemistry.

[8]  Jerzy Leszczynski,et al.  QSPR/QSAR analyses by means of the CORAL software: Results, challenges, perspectives , 2015 .

[9]  Ashwani Kumar,et al.  QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method , 2016, Drug Research.

[10]  Mariya A Toropova,et al.  CORAL Software: Analysis of Impacts of Pharmaceutical Agents Upon Metabolism via the Optimal Descriptors. , 2017, Current drug metabolism.

[11]  E. Benfenati,et al.  Odor threshold prediction by means of the Monte Carlo method. , 2016, Ecotoxicology and environmental safety.

[13]  E. Castro,et al.  Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds , 2016, International journal of molecular sciences.

[14]  Apilak Worachartcheewan,et al.  QSAR Study of H1N1 Neuraminidase Inhibitors from Influenza a Virus , 2014 .

[15]  P. Duchowicz,et al.  QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors , 2015, SAR and QSAR in environmental research.

[16]  James Hartley,et al.  Response Format in Writing Self-Efficacy Assessment: Greater Discrimination Increases Prediction , 2001 .

[17]  I. Gutman,et al.  Relation between second and third geometric–arithmetic indices of trees , 2011 .

[18]  Emilio Benfenati,et al.  QSPR modeling of enthalpies of formation for organometallic compounds by SMART‐based optimal descriptors , 2009, J. Comput. Chem..

[19]  Agnieszka Gajewicz,et al.  What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps. , 2017, Nanoscale.

[20]  O. Raevsky,et al.  Acute toxicity evaluation upon intravenous injection into mice: interspecies correlations, lipophilicity parameters, and physicochemical descriptors , 2012, Pharmaceutical Chemistry Journal.

[21]  Shikha Gupta,et al.  QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes , 2017, Environmental Science and Pollution Research.

[22]  Emilio Benfenati,et al.  QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA's OpenFoodTox database. , 2017, Environmental toxicology and pharmacology.

[23]  Jay Tunkel,et al.  Predicting genotoxicity of aromatic and heteroaromatic amines using electrotopological state indices. , 2005, Mutation research.

[24]  Subhash C. Basak,et al.  Editorial: The Expanding Landscape of Graph Theoretic Molecular Descriptors: Development, Gradual Diversification of Descriptor Space, and Applications in QSAR/ QMSA and New Drug Discovery. , 2017, Current computer-aided drug design.

[25]  Andrey A Toropov,et al.  CORAL: Binary classifications (active/inactive) for drug-induced liver injury. , 2017, Toxicology letters.

[26]  Arafeh Bigdeli,et al.  Towards defining new nano-descriptors: extracting morphological features from transmission electron microscopy images , 2014 .

[27]  A. Ghaedi Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors , 2015 .

[28]  Jerzy Leszczynski,et al.  Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines , 2011 .

[29]  Shilpi Chauhan,et al.  Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors , 2017, Archiv der Pharmazie.

[30]  Sagarika Sahoo,et al.  A Short Review of the Generation of Molecular Descriptors and Their Applications in Quantitative Structure Property/Activity Relationships. , 2016, Current computer-aided drug design.

[31]  J. Tong,et al.  A New Descriptor of Amino Acids‐SVGER and its Applications in Peptide QSAR , 2017, Molecular informatics.

[32]  L. Migliore,et al.  Ecotoxicological Method with Marine Bacteria Vibrio anguillarum to Evaluate the Acute Toxicity of Environmental Contaminants , 2017, Journal of visualized experiments : JoVE.

[33]  Giuseppina C. Gini,et al.  Coral: QSAR models for acute toxicity in fathead minnow (Pimephales promelas) , 2012, J. Comput. Chem..

[34]  Mark T. D. Cronin,et al.  The present status of QSAR in toxicology , 2003 .

[35]  E. Benfenati,et al.  QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors , 2010, Molecular Diversity.

[36]  R. Carbó-Dorca,et al.  Modelling Toxicity using Molecular Quantum Similarity Measures , 2006 .

[37]  Min-Kyeong Yeo,et al.  Ecotoxicity Estimation of Hazardous Air Pollutants Emitted from Semiconductor Manufacturing Processes Utilizing QSAR , 2013 .

[38]  M. Cronin,et al.  Pitfalls in QSAR , 2003 .

[39]  Ataul Islam,et al.  Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors , 2016 .

[40]  Emilio Benfenati,et al.  SMILES as an alternative to the graph in QSAR modelling of bee toxicity , 2007, Comput. Biol. Chem..

[41]  P. Achary,et al.  QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software , 2014, SAR and QSAR in environmental research.

[42]  T W Schultz,et al.  Using chemical categories to fill data gaps in hazard assessment , 2009, SAR and QSAR in environmental research.

[43]  Jerzy Leszczynski,et al.  CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna , 2012 .

[44]  Patrik L. Andersson,et al.  Binary classification model to predict developmental toxicity of industrial chemicals in zebrafish , 2016 .

[45]  Arthur M. Doweyko,et al.  QSAR: dead or alive? , 2008, J. Comput. Aided Mol. Des..

[46]  E. Benfenati,et al.  Comparison of in silico tools for evaluating rat oral acute toxicity† , 2015, SAR and QSAR in environmental research.

[47]  M. Scotti,et al.  Combined structure- and ligand-based virtual screening to evaluate caulerpin analogs with potential inhibitory activity against monoamine oxidase B , 2015 .

[48]  Raghuraman Venkatapathy,et al.  Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals I. Alternative toxicity measures as an estimator of carcinogenic potency. , 2009, Toxicology and applied pharmacology.

[49]  Carmela Parenti,et al.  Development of a Sigma‐2 Receptor affinity filter through a Monte Carlo based QSAR analysis , 2017, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[50]  Z Y Algamal,et al.  A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives , 2017, SAR and QSAR in environmental research.

[51]  Jerzy Leszczynski,et al.  QSAR Modeling of Acute Toxicity for Nitrobenzene Derivatives Towards Rats: Comparative Analysis by MLRA and Optimal Descriptors , 2007 .

[52]  Vesko Milenković,et al.  QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method , 2017, Medicinal Chemistry Research.

[53]  Hongzong Si,et al.  QSAR model based on SMILES of inhibitory rate of 2, 3-diarylpropenoic acids on AKR1C3 , 2014 .

[54]  Paola Gramatica,et al.  QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo‐)triazoles on Algae , 2012, Molecular informatics.

[55]  R. Todeschini,et al.  Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling. , 2012, Chemosphere.

[56]  Development of quantitative structure activity relationships for the binding affinity of methoxypyridinium cations for human acetylcholinesterase. , 2015, Journal of molecular graphics & modelling.

[57]  Emilio Benfenati,et al.  Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: An unexpected good prediction based on a model that seems untrustworthy , 2011 .

[58]  Tomasz Puzyn,et al.  Comparing the CORAL and Random Forest Approaches for Modelling the In Vitro Cytotoxicity of Silica Nanomaterials , 2016, Alternatives to laboratory animals : ATLA.

[59]  Enrico Burello,et al.  Review of (Q)SAR models for regulatory assessment of nanomaterials risks , 2017 .

[60]  M. Goodarzi,et al.  Influence of Changes in 2‐D Chemical Structure Drawings and Image Formats on the Prediction of Biological Properties Using MIA‐QSAR , 2009 .

[61]  E Benfenati,et al.  Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: Using rare SMILES attributes to define the applicability domain. , 2008, Bioorganic & medicinal chemistry.

[62]  H. Kouzuki,et al.  Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients. , 2015, The Journal of toxicological sciences.

[63]  Vesna Rastija,et al.  PyDescriptor : A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors , 2017 .

[64]  Aleksandar M. Veselinović,et al.  Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis , 2016, Structural Chemistry.

[65]  Sharon L. A. Munro,et al.  Comparative review of molecular modelling software for personal computers , 1988, J. Comput. Aided Mol. Des..

[66]  Andrey A. Toropov,et al.  Calculation of total molecular electronic energies from Correlation Weighting of Local Graph Invariants , 2001 .

[67]  D. Sokolović,et al.  QSAR modeling of bis-quinolinium and bis-isoquinolinium compounds as acetylcholine esterase inhibitors based on the Monte Carlo method—the implication for Myasthenia gravis treatment , 2016, Medicinal Chemistry Research.

[68]  M. Gobbi,et al.  Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds. , 2016, Toxicology letters.