QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid
The increasing application of new ionic liquids (IL) creates the need of liquid‐liquid equilibria data for both miscible and quasi‐immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Struct...
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Veröffentlicht in: | Molecular informatics 2020-09, Vol.39 (9), p.e2000001-n/a |
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creator | Klimenko, Kyrylo Oleksandrovych Inês, João Miguel Esperança, José Manuel Silva Simões Rebelo, Luís Paulo Nieto Aires‐de‐Sousa, João Carrera, Gonçalo Valente Silva Mariño |
description | The increasing application of new ionic liquids (IL) creates the need of liquid‐liquid equilibria data for both miscible and quasi‐immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y‐scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two‐phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines. |
doi_str_mv | 10.1002/minf.202000001 |
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In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y‐scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two‐phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.</description><identifier>ISSN: 1868-1743</identifier><identifier>EISSN: 1868-1751</identifier><identifier>DOI: 10.1002/minf.202000001</identifier><identifier>PMID: 32469147</identifier><language>eng</language><publisher>Germany: Wiley Subscription Services, Inc</publisher><subject>Data analysis ; Data points ; ILThermo ; Ionic liquids ; Ions ; Miscibility ; Modelling ; outlier detection ; Outliers (statistics) ; Phase diagrams ; Phase equilibria ; Solvents ; Temperature dependence</subject><ispartof>Molecular informatics, 2020-09, Vol.39 (9), p.e2000001-n/a</ispartof><rights>2020 Wiley‐VCH Verlag GmbH & Co. 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In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y‐scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two‐phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.</description><subject>Data analysis</subject><subject>Data points</subject><subject>ILThermo</subject><subject>Ionic liquids</subject><subject>Ions</subject><subject>Miscibility</subject><subject>Modelling</subject><subject>outlier detection</subject><subject>Outliers (statistics)</subject><subject>Phase diagrams</subject><subject>Phase equilibria</subject><subject>Solvents</subject><subject>Temperature dependence</subject><issn>1868-1743</issn><issn>1868-1751</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFkMtKAzEUhoMottRuXUrAjZupydyzlNJqofXWihthSDJnNGUubdKhdOcj-Iw-iRlbK7jxLHIO5Dsfhx-hU0p6lBD3slBl1nOJS5qiB6hN4zB2aBTQw_3sey3UNWbeIJ4bRjE7Ri3P9UNG_aiNXh6m9494UqWQq_IVVxkeq2Wt0s_3j_x7wAP75kpoxbEq8Wxd2a_FGzeApxuzgsI0S898BRrzMsWjqlRyJzlBRxnPDXR3vYOehoNZ_8YZ312P-ldjR_oBoQ6lknKfyRg8xkPKZWqPE5kQQEUEIYgMpCtY4KceEySS0vUkA0ZkRkgWytjroIutd6GrZQ1mlRTKSMhzXkJVm8T1SUwZI0Fg0fM_6LyqdWmvs5THbF5xEFmqt6WkrozRkCULrQquNwklSRN90kSf7KO3C2c7bS0KSPf4T9AWYFtgrXLY_KNLJqPb4a_8CwkKkSo</recordid><startdate>202009</startdate><enddate>202009</enddate><creator>Klimenko, Kyrylo Oleksandrovych</creator><creator>Inês, João Miguel</creator><creator>Esperança, José Manuel Silva Simões</creator><creator>Rebelo, Luís Paulo Nieto</creator><creator>Aires‐de‐Sousa, João</creator><creator>Carrera, Gonçalo Valente Silva Mariño</creator><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TM</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7250-7300</orcidid></search><sort><creationdate>202009</creationdate><title>QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid</title><author>Klimenko, Kyrylo Oleksandrovych ; Inês, João Miguel ; Esperança, José Manuel Silva Simões ; Rebelo, Luís Paulo Nieto ; Aires‐de‐Sousa, João ; Carrera, Gonçalo Valente Silva Mariño</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4501-11c1a49c8e39a61acd691bfbbe1b7e6ebfec2b954d39b07cc23c9e90cf00f6c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Data analysis</topic><topic>Data points</topic><topic>ILThermo</topic><topic>Ionic liquids</topic><topic>Ions</topic><topic>Miscibility</topic><topic>Modelling</topic><topic>outlier detection</topic><topic>Outliers (statistics)</topic><topic>Phase diagrams</topic><topic>Phase equilibria</topic><topic>Solvents</topic><topic>Temperature dependence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Klimenko, Kyrylo Oleksandrovych</creatorcontrib><creatorcontrib>Inês, João Miguel</creatorcontrib><creatorcontrib>Esperança, José Manuel Silva Simões</creatorcontrib><creatorcontrib>Rebelo, Luís Paulo Nieto</creatorcontrib><creatorcontrib>Aires‐de‐Sousa, João</creatorcontrib><creatorcontrib>Carrera, Gonçalo Valente Silva Mariño</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Molecular informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Klimenko, Kyrylo Oleksandrovych</au><au>Inês, João Miguel</au><au>Esperança, José Manuel Silva Simões</au><au>Rebelo, Luís Paulo Nieto</au><au>Aires‐de‐Sousa, João</au><au>Carrera, Gonçalo Valente Silva Mariño</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid</atitle><jtitle>Molecular informatics</jtitle><addtitle>Mol Inform</addtitle><date>2020-09</date><risdate>2020</risdate><volume>39</volume><issue>9</issue><spage>e2000001</spage><epage>n/a</epage><pages>e2000001-n/a</pages><issn>1868-1743</issn><eissn>1868-1751</eissn><abstract>The increasing application of new ionic liquids (IL) creates the need of liquid‐liquid equilibria data for both miscible and quasi‐immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y‐scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two‐phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.</abstract><cop>Germany</cop><pub>Wiley Subscription Services, Inc</pub><pmid>32469147</pmid><doi>10.1002/minf.202000001</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-7250-7300</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Data analysis Data points ILThermo Ionic liquids Ions Miscibility Modelling outlier detection Outliers (statistics) Phase diagrams Phase equilibria Solvents Temperature dependence |
title | QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid |
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