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Tan T, Cheng H, Chen G, Song Z, Qi Z. Prediction of infinite‐dilution activity coefficients with neural collaborative filtering. AIChE J 2022. [DOI: 10.1002/aic.17789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Tian Tan
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Hongye Cheng
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Guzhong Chen
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Zhen Song
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Zhiwen Qi
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
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2
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Jeon HN, Shin HK, Hwang S, No KT. Development of an Infinite Dilution Activity Coefficient Prediction Model for Organic Solutes in Ionic Liquids with Modified Partial Equalization Orbital Electronegativity Method Derived Descriptors. ACS OMEGA 2021; 6:15361-15373. [PMID: 34151114 PMCID: PMC8210453 DOI: 10.1021/acsomega.1c01690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/25/2021] [Indexed: 06/13/2023]
Abstract
The objective of this study was to develop a robust prediction model for the infinite dilution activity coefficients (γ ∞) of organic molecules in diverse ionic liquid (IL) solvents. Electrostatic, hydrogen bond, polarizability, molecular structure, and temperature terms were used in model development. A feed-forward model based on artificial neural networks was developed with 34,754 experimental activity coefficients, a combination of 195 IL solvents (88 cations and 38 anions), and 147 organic solutes at a temperature range of 298 to 408 K. The root mean squared error (RMSE) of the training set and test set was 0.219 and 0.235, respectively. The R 2 of the training set and the test set was 0.984 and 0.981, respectively. The applicability domain was determined through a Williams plot, which implied that water and halogenated compounds were outside of the applicability domain. The robustness test shows that the developed model is robust. The web server supports using the developed prediction model and is freely available at https://preadmet.bmdrc.kr/activitycoefficient_mainpage/prediction/.
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Affiliation(s)
- Hyeon-Nae Jeon
- Department
of Biotechnology, Yonsei University, Yonsei-ro 50, Seoul 03722, Republic
of Korea
| | - Hyun Kil Shin
- Department
of Biotechnology, Yonsei University, Yonsei-ro 50, Seoul 03722, Republic
of Korea
| | - Sungbo Hwang
- Department
of Biotechnology, Yonsei University, Yonsei-ro 50, Seoul 03722, Republic
of Korea
| | - Kyoung Tai No
- Department
of Biotechnology, Yonsei University, Yonsei-ro 50, Seoul 03722, Republic
of Korea
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3
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Jirasek F, Alves RAS, Damay J, Vandermeulen RA, Bamler R, Bortz M, Mandt S, Kloft M, Hasse H. Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion. J Phys Chem Lett 2020; 11:981-985. [PMID: 31964142 DOI: 10.1021/acs.jpclett.9b03657] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physicochemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering.
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Affiliation(s)
- Fabian Jirasek
- Department of Computer Science , University of California , Irvine , California 92697 , United States
- Laboratory of Engineering Thermodynamics (LTD) , TU Kaiserslautern , 67663 Kaiserslautern , Germany
| | - Rodrigo A S Alves
- Machine Learning Group, Department of Computer Science , TU Kaiserslautern , 67663 Kaiserslautern , Germany
| | - Julie Damay
- Fraunhofer Institute for Industrial Mathematics ITWM , 67663 Kaiserslautern , Germany
| | - Robert A Vandermeulen
- Machine Learning Group, Department of Computer Science , TU Kaiserslautern , 67663 Kaiserslautern , Germany
| | - Robert Bamler
- Department of Computer Science , University of California , Irvine , California 92697 , United States
| | - Michael Bortz
- Fraunhofer Institute for Industrial Mathematics ITWM , 67663 Kaiserslautern , Germany
| | - Stephan Mandt
- Department of Computer Science , University of California , Irvine , California 92697 , United States
| | - Marius Kloft
- Machine Learning Group, Department of Computer Science , TU Kaiserslautern , 67663 Kaiserslautern , Germany
| | - Hans Hasse
- Laboratory of Engineering Thermodynamics (LTD) , TU Kaiserslautern , 67663 Kaiserslautern , Germany
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4
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Modeling the activity coefficient at infinite dilution of water in ionic liquids using artificial neural networks and support vector machines. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04356-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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5
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Fayet G, Rotureau P. New QSPR Models to Predict the Flammability of Binary Liquid Mixtures. Mol Inform 2019; 38:e1800122. [DOI: 10.1002/minf.201800122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 12/12/2018] [Indexed: 12/14/2022]
Affiliation(s)
- Guillaume Fayet
- INERISAccidental Risk Division Parc Technologique Alata 60550 Verneuil-en-Halatte France
| | - Patricia Rotureau
- INERISAccidental Risk Division Parc Technologique Alata 60550 Verneuil-en-Halatte France
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6
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Moghadam M, Asgharzadeh S. On the application of artificial neural network for modeling liquid-liquid equilibrium. J Mol Liq 2016. [DOI: 10.1016/j.molliq.2016.04.098] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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7
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Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov Today 2015; 21:225-38. [PMID: 26360051 DOI: 10.1016/j.drudis.2015.09.003] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/30/2015] [Accepted: 09/01/2015] [Indexed: 01/18/2023]
Abstract
The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.
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Gaudin T, Rotureau P, Fayet G. Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01457] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Théophile Gaudin
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
| | - Patricia Rotureau
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
| | - Guillaume Fayet
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
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9
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Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014; 57:4977-5010. [PMID: 24351051 PMCID: PMC4074254 DOI: 10.1021/jm4004285] [Citation(s) in RCA: 1053] [Impact Index Per Article: 105.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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Affiliation(s)
- Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexandre Varnek
- Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
| | - Igor I. Baskin
- Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - John Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - Paola Gramatica
- Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
| | | | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Victor E. Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | | | - Romualdo Benigni
- Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy
| | | | - James Rathman
- Altamira LLC, Columbus OH 43235, USA
- Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA
| | | | | | - Ann Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27519, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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10
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Ajmani S, Janardhan S, Viswanadhan VN. Toward a general predictive QSAR model for gamma-secretase inhibitors. Mol Divers 2013; 17:421-34. [DOI: 10.1007/s11030-013-9441-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2013] [Accepted: 03/26/2013] [Indexed: 10/26/2022]
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11
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Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM). J Cheminform 2013; 5:4. [PMID: 23321019 PMCID: PMC3568005 DOI: 10.1186/1758-2946-5-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Accepted: 01/08/2013] [Indexed: 11/10/2022] Open
Abstract
The Online Chemical Modeling Environment (OCHEM, http://ochem.eu) is a web-based platform that provides tools for automation of typical steps necessary to create a predictive QSAR/QSPR model. The platform consists of two major subsystems: a database of experimental measurements and a modeling framework. So far, OCHEM has been limited to the processing of individual compounds. In this work, we extended OCHEM with a new ability to store and model properties of binary non-additive mixtures. The developed system is publicly accessible, meaning that any user on the Web can store new data for binary mixtures and develop models to predict their non-additive properties.The database already contains almost 10,000 data points for the density, bubble point, and azeotropic behavior of binary mixtures. For these data, we developed models for both qualitative (azeotrope/zeotrope) and quantitative endpoints (density and bubble points) using different learning methods and specially developed descriptors for mixtures. The prediction performance of the models was similar to or more accurate than results reported in previous studies. Thus, we have developed and made publicly available a powerful system for modeling mixtures of chemical compounds on the Web.
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12
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Oprisiu I, Varlamova E, Muratov E, Artemenko A, Marcou G, Polishchuk P, Kuz'min V, Varnek A. QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids. Mol Inform 2012; 31:491-502. [DOI: 10.1002/minf.201200006] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 04/23/2012] [Indexed: 11/11/2022]
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13
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Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and Developing Approaches for QSAR Analysis of Mixtures. Mol Inform 2012; 31:202-21. [PMID: 27477092 DOI: 10.1002/minf.201100129] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2011] [Accepted: 02/04/2012] [Indexed: 11/10/2022]
Abstract
This review is devoted to the critical analysis of advantages and disadvantages of existing mixture descriptors and their usage in various QSAR/QSPR tasks. We describe good practices for the QSAR modeling of mixtures, data sources for mixtures, a discussion of various mixture descriptors and their application, recommendations about proper external validation specific for mixture QSAR modeling, and future perspectives of this field. The biggest problem in QSAR of mixtures is the lack of reliable data about the mixtures' properties. Various mixture descriptors are used for the modeling of different endpoints. However, these descriptors have certain disadvantages, such as applicability only to 1 : 1 binary mixtures, and additive nature. The field of QSAR of mixtures is still under development, and existing efforts could be considered as a foundation for future approaches and studies. The usage of non-additive mixture descriptors, which are sensitive to interaction effects, in combination with best practices of QSAR model development (e.g., thorough data collection and curation, rigorous external validation, etc.) will significantly improve the quality of QSAR studies of mixtures.
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Affiliation(s)
- Eugene N Muratov
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394. , .,Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, Eshelman School of Pharmacy, University of North Carolina, Beard Hall 301, CB#7568, Chapel Hill, NC, 27599, USA tel: +19199663459, fax: +19199660204. ,
| | - Ekaterina V Varlamova
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Anatoly G Artemenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Pavel G Polishchuk
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Victor E Kuz'min
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
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14
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Solov’ev VP, Oprisiu I, Marcou G, Varnek A. Quantitative Structure–Property Relationship (QSPR) Modeling of Normal Boiling Point Temperature and Composition of Binary Azeotropes. Ind Eng Chem Res 2011. [DOI: 10.1021/ie2018614] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Vitaly P. Solov’ev
- Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Leninskiy prospect, 31a, 119991, Moscow, Russia
| | - Ioana Oprisiu
- Laboratoire d’Infochimie, UMR 7177 CNRS, Université de Strasbourg, 4, rue B.Pascal, Strasbourg, 67000, France
| | - Gilles Marcou
- Laboratoire d’Infochimie, UMR 7177 CNRS, Université de Strasbourg, 4, rue B.Pascal, Strasbourg, 67000, France
| | - Alexandre Varnek
- Laboratoire d’Infochimie, UMR 7177 CNRS, Université de Strasbourg, 4, rue B.Pascal, Strasbourg, 67000, France
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15
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Katritzky AR, Stoyanova-Slavova IB, Tämm K, Tamm T, Karelson M. Application of the QSPR Approach to the Boiling Points of Azeotropes. J Phys Chem A 2011; 115:3475-9. [DOI: 10.1021/jp104287p] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Alan R. Katritzky
- Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Iva B. Stoyanova-Slavova
- Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Kaido Tämm
- Institute of Chemistry, University of Tartu, Ravila 14A, 50411 Tartu, Estonia
| | - Tarmo Tamm
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | - Mati Karelson
- Institute of Chemistry, University of Tartu, Ravila 14A, 50411 Tartu, Estonia
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16
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Ajmani S, Rogers SC, Barley MH, Burgess AN, Livingstone DJ. Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks. Mol Inform 2010; 29:645-53. [PMID: 27463458 DOI: 10.1002/minf.201000027] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2010] [Accepted: 08/20/2010] [Indexed: 11/11/2022]
Abstract
In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures.
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Affiliation(s)
- Subhash Ajmani
- Centre for Molecular Design, Institute of Biomedical and Biomolecular Science, University of Portsmouth, King Henry 1 Street, Portsmouth PO1 2DY, UK, Tel/Fax: +91-20-27292268. .,Present Address: NovaLead Pharma Pvt. Ltd. Baner Road, Pune, India.
| | | | - Mark H Barley
- School of Earth, Atmospheric and Environmental Science, University of Manchester, Oxford Road, Manchester M13 9PL
| | | | - David J Livingstone
- Centre for Molecular Design, Institute of Biomedical and Biomolecular Science, University of Portsmouth, King Henry 1 Street, Portsmouth PO1 2DY, UK, Tel/Fax: +91-20-27292268.,ChemQuest, Delamere House, 1 Royal Crescent, Sandown, Isle of Wight, PO36 8LZ UK
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