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Llompart P, Minoletti C, Baybekov S, Horvath D, Marcou G, Varnek A. Will we ever be able to accurately predict solubility? Sci Data 2024; 11:303. [PMID: 38499581 PMCID: PMC10948805 DOI: 10.1038/s41597-024-03105-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/29/2024] [Indexed: 03/20/2024] Open
Abstract
Accurate prediction of thermodynamic solubility by machine learning remains a challenge. Recent models often display good performances, but their reliability may be deceiving when used prospectively. This study investigates the origins of these discrepancies, following three directions: a historical perspective, an analysis of the aqueous solubility dataverse and data quality. We investigated over 20 years of published solubility datasets and models, highlighting overlooked datasets and the overlaps between popular sets. We benchmarked recently published models on a novel curated solubility dataset and report poor performances. We also propose a workflow to cure aqueous solubility data aiming at producing useful models for bench chemist. Our results demonstrate that some state-of-the-art models are not ready for public usage because they lack a well-defined applicability domain and overlook historical data sources. We report the impact of factors influencing the utility of the models: interlaboratory standard deviation, ionic state of the solute and data sources. The herein obtained models, and quality-assessed datasets are publicly available.
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Affiliation(s)
- P Llompart
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
- IDD/CADD, Sanofi, Vitry-Sur-Seine, France
| | | | - S Baybekov
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
| | - D Horvath
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
| | - G Marcou
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France.
| | - A Varnek
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
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2
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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3
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Abraham MH, Acree WE, Liu X. Descriptors for High‐Energy Nitro Compounds; Estimation of Thermodynamic, Physicochemical and Environmental Properties. PROPELLANTS EXPLOSIVES PYROTECHNICS 2021. [DOI: 10.1002/prep.202000117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Michael H. Abraham
- Department of Chemistry University College London, 20 Gordon St London WC1H, 0AJ UK
| | - William E. Acree
- Department of Chemistry 1155 Union Circle Drive #305070 University of North Texas Denton, TX 76203-5017 USA
| | - Xiangli Liu
- School of Pharmacy and Medical Sciences Faculty of Life Sciences University of Bradford Bradford BD7 1DP UK
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4
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Development of quantitative structure-property relationship (QSPR) models for predicting the thermal hazard of ionic liquids: A review of methods and models. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.112471] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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5
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Benfenati E, Chaudhry Q, Gini G, Dorne JL. Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy. ENVIRONMENT INTERNATIONAL 2019; 131:105060. [PMID: 31377600 DOI: 10.1016/j.envint.2019.105060] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 06/26/2019] [Accepted: 07/25/2019] [Indexed: 06/10/2023]
Abstract
In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.
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Affiliation(s)
- Emilio Benfenati
- Department of Environmental and Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano, Italy.
| | - Qasim Chaudhry
- University of Chester, Parkgate Road, Chester CH1 4BJ, United Kingdom
| | | | - Jean Lou Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, Parma, Italy
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6
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Shen S, Pan Y, Ji X, Ni Y, Jiang J. Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures. Int J Mol Sci 2019; 20:ijms20092084. [PMID: 31035591 PMCID: PMC6539801 DOI: 10.3390/ijms20092084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 04/11/2019] [Accepted: 04/23/2019] [Indexed: 11/17/2022] Open
Abstract
A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous “compounds out” strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.
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Affiliation(s)
- Shijing Shen
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Xianke Ji
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Yuqing Ni
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
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Klimenko K, Kuz'min V, Ognichenko L, Gorb L, Shukla M, Vinas N, Perkins E, Polishchuk P, Artemenko A, Leszczynski J. Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility. J Comput Chem 2016; 37:2045-51. [PMID: 27338156 DOI: 10.1002/jcc.24424] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2016] [Revised: 04/22/2016] [Accepted: 05/17/2016] [Indexed: 11/09/2022]
Abstract
A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure-property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation lgSw=kT+c, where Sw is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high-efficiency QSPR model. The performance of the model is assessed using cross-validation and external test set prediction. Predictive capacity of developed model is compared with COSMO-RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Kyrylo Klimenko
- Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa, 65080, Ukraine.,Laboratoire de Chemoinformatique, (UMR 7140 CNRS/UniStra) Université de Strasbourg, 1, rue B. Pascal, Strasbourg, 67000, France
| | - Victor Kuz'min
- Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa, 65080, Ukraine
| | - Liudmila Ognichenko
- Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa, 65080, Ukraine
| | | | - Manoj Shukla
- US Army Engineer Research and Development Center, Vicksburg, Mississippi, 39180
| | - Natalia Vinas
- US Army Engineer Research and Development Center, Vicksburg, Mississippi, 39180
| | - Edward Perkins
- US Army Engineer Research and Development Center, Vicksburg, Mississippi, 39180
| | - Pavel Polishchuk
- Institute of Molecular and Translational Medicine, Palacky University Olomouc, Hnevotínská 1333/5, Olomouc, 779 00, Czech Republic
| | - Anatoly Artemenko
- Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa, 65080, Ukraine
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Jackson State University, Jackson, Mississippi, 39217
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8
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Computational assessment of environmental hazards of nitroaromatic compounds: influence of the type and position of aromatic ring substituents on toxicity. Struct Chem 2015. [DOI: 10.1007/s11224-015-0715-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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10
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Polishchuk PG, Samoylenko GV, Khristova TM, Krysko OL, Kabanova TA, Kabanov VM, Kornylov AY, Klimchuk O, Langer T, Andronati SA, Kuz'min VE, Krysko AA, Varnek A. Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents. J Med Chem 2015; 58:7681-94. [PMID: 26367138 DOI: 10.1021/acs.jmedchem.5b00865] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
This article describes design, virtual screening, synthesis, and biological tests of novel αIIbβ3 antagonists, which inhibit platelet aggregation. Two types of αIIbβ3 antagonists were developed: those binding either closed or open form of the protein. At the first step, available experimental data were used to build QSAR models and ligand- and structure-based pharmacophore models and to select the most appropriate tool for ligand-to-protein docking. Virtual screening of publicly available databases (BioinfoDB, ZINC, Enamine data sets) with developed models resulted in no hits. Therefore, small focused libraries for two types of ligands were prepared on the basis of pharmacophore models. Their screening resulted in four potential ligands for open form of αIIbβ3 and four ligands for its closed form followed by their synthesis and in vitro tests. Experimental measurements of affinity for αIIbβ3 and ability to inhibit ADP-induced platelet aggregation (IC50) showed that two designed ligands for the open form 4c and 4d (IC50 = 6.2 nM and 25 nM, respectively) and one for the closed form 12b (IC50 = 11 nM) were more potent than commercial antithrombotic Tirofiban (IC50 = 32 nM).
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Affiliation(s)
- Pavel G Polishchuk
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Georgiy V Samoylenko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Tetiana M Khristova
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine.,Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
| | - Olga L Krysko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Tatyana A Kabanova
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Vladimir M Kabanov
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Alexander Yu Kornylov
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Olga Klimchuk
- Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
| | - Sergei A Andronati
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Victor E Kuz'min
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Andrei A Krysko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Alexandre Varnek
- Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
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11
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Salahinejad M, Le TC, Winkler DA. Aqueous Solubility Prediction: Do Crystal Lattice Interactions Help? Mol Pharm 2013; 10:2757-66. [DOI: 10.1021/mp4001958] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Maryam Salahinejad
- Faculty of Chemistry, Tarbiat Moallem University, Tehran 15719-14911, Iran
- CSIRO Materials Science & Engineering, Clayton 3168, Australia
- Monash Institute of Pharmaceutical Sciences, Parkville 3052, Australia
| | - Tu C. Le
- CSIRO Materials Science & Engineering, Clayton 3168, Australia
| | - David A. Winkler
- CSIRO Materials Science & Engineering, Clayton 3168, Australia
- Monash Institute of Pharmaceutical Sciences, Parkville 3052, Australia
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12
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Ognichenko LN, Kuz'min VE, Gorb L, Hill FC, Artemenko AG, Polischuk PG, Leszczynski J. QSPR Prediction of Lipophilicity for Organic Compounds Using Random Forest Technique on the Basis of Simplex Representation of Molecular Structure. Mol Inform 2012; 31:273-80. [PMID: 27477097 DOI: 10.1002/minf.201100102] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 02/05/2012] [Indexed: 11/08/2022]
Abstract
The relationship between the octanol-water partition coefficient for more than twelve thousand organic compounds and their structures was investigated using a QSPR approach based on Simplex Representation of Molecular Structure (SiRMS). The dataset used in our study included 10973 compounds with experimental values of lipophilicity (LogKow ) for different chemical compounds. Random Forest (RF) method was used for statistical modeling at the 2D level of representation of molecular structure. Developed models are adequate and successfully validated with external test sets. Proposed models have clear interpretation due to the use of simplex representation of molecular structure and predict the LogKow values with the accuracy of the best modern models. Thus QSPR models proposed in this study represent powerful and easy-to use virtual screening tool that can be recommended for prediction of octanol-water partition coefficient.
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Affiliation(s)
- Liudmyla N Ognichenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Victor E Kuz'min
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Leonid Gorb
- Badger Technical Services, LLC, Vicksburg, Mississippi, USA
| | - Frances C Hill
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA
| | - Anatoly G Artemenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Pavel G Polischuk
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Jerzy Leszczynski
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA. .,Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, 39217, USA.
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13
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Zeng XL, Wang HJ, Wang Y. QSPR models of n-octanol/water partition coefficients and aqueous solubility of halogenated methyl-phenyl ethers by DFT method. CHEMOSPHERE 2012; 86:619-625. [PMID: 22115466 DOI: 10.1016/j.chemosphere.2011.10.051] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2011] [Revised: 10/24/2011] [Accepted: 10/24/2011] [Indexed: 05/31/2023]
Abstract
The possible molecular geometries of 134 halogenated methyl-phenyl ethers were optimized at B3LYP/6-31G(*) level with Gaussian 98 program. The calculated structural parameters were taken as theoretical descriptors to establish two new novel QSPR models for predicting aqueous solubility (-lgS(w,l)) and n-octanol/water partition coefficient (lgK(ow)) of halogenated methyl-phenyl ethers. The two models achieved in this work both contain three variables: energy of the lowest unoccupied molecular orbital (E(LUMO)), most positive atomic partial charge in molecule (q(+)), and quadrupole moment (Q(yy) or Q(zz)), of which R values are 0.992 and 0.970 respectively, their standard errors of estimate in modeling (SD) are 0.132 and 0.178, respectively. The results of leave-one-out (LOO) cross-validation for training set and validation with external test sets both show that the models obtained exhibited optimum stability and good predictive power. We suggests that two QSPR models derived here can be used to predict S(w,l) and K(ow) accurately for non-tested halogenated methyl-phenyl ethers congeners.
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Affiliation(s)
- Xiao-Lan Zeng
- Department of Chemistry and Chemical Engineering, Xinyang Normal University, Henan Xinyang 464000, People's Republic of China.
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