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Prediction of the adsorption capability onto activated carbon of a large data set of chemicals by local lazy regression method
Authors:Beilei Lei  Yimeng Ma  Jiazhong Li  Huanxiang Liu  Xiaojun Yao  Paola Gramatica
Institution:1. State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou 730000, China;2. School of Pharmacy, Lanzhou University, Lanzhou 730000, China;3. QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese21100, Italy;1. Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent 9000, Belgium;2. Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, Ghent University, Ghent 9000, Belgium;1. Department of Environmental and Municipal Engineering, Tianjin Key Laboratory of Aquatic Science and Technology, Tianjin Chengjian University, Jinjing Road 26, Tianjin 300384, China;2. Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia;3. Department of Environmental Sciences and Engineering, Tianjin University, Wei Jin Road 92, Tianjin 300072, China;4. Tianjin Sino French Jieyuan Water Company Limited, Jieyuan Road 30, Tianjin 300121, China;1. Division of Pharmaceutical Chemistry, Dr. B C Roy College of Pharmacy & Allied Health Sciences, Bidhannagar, Durgapur 713 206, India;2. Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
Abstract:Accurate quantitative structure–property relationship (QSPR) models based on a large data set containing a total of 3483 organic compounds were developed to predict chemicals’ adsorption capability onto activated carbon in gas phrase. Both global multiple linear regression (MLR) method and local lazy regression (LLR) method were used to develop QSPR models. The results proved that LLR has prediction accuracy 10% higher than that of MLR model. By applying LLR method we can predict the test set (787 compounds) with Q2ext of 0.900 and root mean square error (RMSE) of 0.129. The accurate model based on this large data set could be useful to predict adsorption property of new compounds since such model covers a highly diverse structural space.
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