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Su L, Wang Z, Wang Y, Xiao Z, Xia D, Zhang S, Chen J. Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:108846-108854. [PMID: 37759049 DOI: 10.1007/s11356-023-29962-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
With an increase in production and application of various engineering nanomaterials (ENMs), they will inevitably be released into the environment. Adsorption of various organic chemicals onto ENMs will impact on their environmental behavior and toxicology. It is unrealistic to experimentally determine adsorption equilibrium constants (K) for the vast number of organics and ENMs due to high cost in expenditure and time. Herein, appropriate molecular dynamics (MD) methods were evaluated and selected by comparing experimental K values of seven organics adsorbed onto graphene with the MD-calculated ones. Machine learning (ML) models on K of organics adsorption onto graphene and black phosphorus nanomaterials were constructed based on a benchmark data set from the MD simulations. Lasso models based on Mordred descriptors outperformed ML models built by support vector machine, random forest, k-nearest neighbor, and gradient boosting decision tree, in terms of cross-validation coefficients (Q2 > 0.90). The Lasso models also outperformed conventional poly-parameter linear free energy relationship models for predicting logK. Compared with previous models, the Lasso models considered more compounds with different functional groups and thus have broader applicability domains. This study provides a promising way to fill the data gap in logK for chemicals adsorbed onto the ENMs.
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Affiliation(s)
- Lihao Su
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Zhongyu Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Ya Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Zijun Xiao
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Deming Xia
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Siyu Zhang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
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2
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A support vector machine analysis to predict density of mixtures of methanol and six ionic liquids. MONATSHEFTE FUR CHEMIE 2018. [DOI: 10.1007/s00706-018-2297-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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3
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Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review. Adv Colloid Interface Sci 2017; 245:20-39. [PMID: 28473053 DOI: 10.1016/j.cis.2017.04.015] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 04/24/2017] [Accepted: 04/24/2017] [Indexed: 11/20/2022]
Abstract
Artificial neural networks (ANNs) have been widely applied for the prediction of dye adsorption during the last decade. In this paper, the applications of ANN methods, namely multilayer feedforward neural networks (MLFNN), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS) for adsorption of dyes are reviewed. The reported researches on adsorption of dyes are classified into four major categories, such as (i) MLFNN, (ii) ANFIS, (iii) SVM and (iv) hybrid with genetic algorithm (GA) and particle swarm optimization (PSO). Most of these papers are discussed. The further research needs in this field are suggested. These ANNs models are obtaining popularity as approaches, which can be successfully employed for the adsorption of dyes with acceptable accuracy.
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4
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Statistical methods and molecular docking for the prediction of thyroid hormone receptor subtype binding affinity and selectivity. Struct Chem 2016. [DOI: 10.1007/s11224-016-0876-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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5
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Support Vector Regression Based QSPR for the Prediction of Retention Time of Peptides in Reversed-Phase Liquid Chromatography. Chromatographia 2014. [DOI: 10.1007/s10337-014-2819-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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6
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Babushok VI, Zenkevich IG. Retention Characteristics of Peptides in RP-LC: Peptide Retention Prediction. Chromatographia 2010. [DOI: 10.1365/s10337-010-1721-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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7
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Application of QSPR for prediction of percent conversion of esterification reactions in supercritical carbon dioxide using least squares support vector regression. J Supercrit Fluids 2010. [DOI: 10.1016/j.supflu.2010.04.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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8
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Tian F, Yang L, Lv F, Zhou P. Modeling and prediction of retention behavior of histidine-containing peptides in immobilized metal-affinity chromatography. J Sep Sci 2009; 32:2159-69. [DOI: 10.1002/jssc.200800739] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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9
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Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches. Anal Chim Acta 2009; 644:10-6. [DOI: 10.1016/j.aca.2009.04.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2008] [Revised: 03/29/2009] [Accepted: 04/07/2009] [Indexed: 11/22/2022]
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10
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Wang B, Chen J, Li X, Wang YN, Chen L, Zhu M, Yu H, Kühne R, Schüürmann G. Estimation of Soil Organic Carbon Normalized Sorption Coefficient (Koc) Using Least Squares-Support Vector Machine. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200860065] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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11
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Zhou P, Tian F, Lv F, Shang Z. Comprehensive comparison of eight statistical modelling methods used in quantitative structure–retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. J Chromatogr A 2009; 1216:3107-16. [PMID: 19232620 DOI: 10.1016/j.chroma.2009.01.086] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2008] [Revised: 01/24/2009] [Accepted: 01/27/2009] [Indexed: 12/30/2022]
Affiliation(s)
- Peng Zhou
- Department of Chemistry, Zhejiang University, Hangzhou, China
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12
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Yang SS, Lu WC, Gu TH, Yan LM, Li GZ. QSPR Study of n
-Octanol/Water Partition Coefficient of Some Aromatic Compounds Using Support Vector Regression. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200810025] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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13
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Gong Z, Xia B, Zhang R, Zhang X, Fan B. Quantitative Structure-Activity Relationship Study on Fish Toxicity of Substituted Benzenes. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200710096] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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14
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Shinoda K, Sugimoto M, Tomita M, Ishihama Y. Informatics for peptide retention properties in proteomic LC-MS. Proteomics 2008; 8:787-98. [PMID: 18214845 DOI: 10.1002/pmic.200700692] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Retention times in HPLC yield valuable information for the identification of various analytes and the prediction of peptide retention is useful for the identification of peptides/proteins in LC-MS-based proteomics. Informatics methods such as artificial neural networks and support vector machines capable of solving nonlinear problems made possible the accurate modeling of quantitative structure-retention relationships of peptides (including large polymers) up to 5 kDa to which classical linear models cannot be applied, as well as the proteome-wide prediction of peptide retention. Proteome-wide retention prediction and accurate mass-information facilitate the identification of peptides in complex proteomic samples. In this review, we address recent developments in solid informatics methods and their application to peptide-retention properties in 'bottom-up' shotgun proteomics. We also describe future prospects for the standardization and application of retention times.
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Affiliation(s)
- Kosaku Shinoda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
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15
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Xia B, Ma W, Zhang X, Fan B. Quantitative structure-retention relationships for organic pollutants in biopartitioning micellar chromatography. Anal Chim Acta 2007; 598:12-8. [PMID: 17693301 DOI: 10.1016/j.aca.2007.07.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2007] [Revised: 06/09/2007] [Accepted: 07/05/2007] [Indexed: 01/30/2023]
Abstract
Quantitative structure-retention relationship (QSRR) models have been successfully developed for the prediction of the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 66 organic pollutants. Heuristic method (HM) and radial basis function neural networks (RBFNN) were utilized to construct the linear and non-linear QSRR models, respectively. The optimal QSRR model was developed based on a 6-17-1 radial basis function neural network architecture using molecular descriptors calculated from molecular structure alone. The RBFNN model gave a correlation coefficient (R2) of 0.8464 and root-mean-square error (RMSE) of 0.1925 for the test set. This paper provided a useful model for the predicting the log k of other organic compounds when experiment data are unknown.
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Affiliation(s)
- Binbin Xia
- Department of Chemistry, Lanzhou University, Lanzhou 730000, Gansu, PR China
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16
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Héberger K. Quantitative structure-(chromatographic) retention relationships. J Chromatogr A 2007; 1158:273-305. [PMID: 17499256 DOI: 10.1016/j.chroma.2007.03.108] [Citation(s) in RCA: 268] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2007] [Revised: 03/13/2007] [Accepted: 03/19/2007] [Indexed: 01/30/2023]
Abstract
Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.
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Affiliation(s)
- Károly Héberger
- Chemical Research Center, Hungarian Academy of Sciences, P.O. Box 17, H-1525 Budapest, Hungary.
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17
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David V, Medvedovici A. Structure‐Retention Correlation in Liquid Chromatography for Pharmaceutical Applications. J LIQ CHROMATOGR R T 2007. [DOI: 10.1080/10826070701191052] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Victor David
- a Department of Analytical Chemistry , University of Bucharest , Bucharest , Romania
| | - Andrei Medvedovici
- a Department of Analytical Chemistry , University of Bucharest , Bucharest , Romania
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18
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Li S, Yao X, Liu H, Li J, Fan B. Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties. Anal Chim Acta 2007; 584:37-42. [PMID: 17386582 DOI: 10.1016/j.aca.2006.11.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2006] [Revised: 11/07/2006] [Accepted: 11/08/2006] [Indexed: 10/23/2022]
Abstract
T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.
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Affiliation(s)
- Shuyan Li
- Department of Chemistry, Lanzhou University, Lanzhou 730000, China
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19
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Petritis K, Kangas LJ, Yan B, Monroe ME, Strittmatter EF, Qian WJ, Adkins JN, Moore RJ, Xu Y, Lipton MS, Camp DG, Smith RD. Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. Anal Chem 2006; 78:5026-39. [PMID: 16841926 PMCID: PMC1924966 DOI: 10.1021/ac060143p] [Citation(s) in RCA: 152] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed-phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest-neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using approximately 345,000 nonredundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of approximately 1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis, K.; Kangas, L. J.; Ferguson, P. L.; Anderson, G. A.; Pasa-Tolic, L.; Lipton, M. S.; Auberry, K. J.; Strittmatter, E. F.; Shen, Y.; Zhao, R.; Smith, R. D. Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.
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Affiliation(s)
- Konstantinos Petritis
- Biological Sciences Division, Environmental and Molecular Sciences Laboratory, P. O. Box 999, Richland, Washington 99352, USA
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Feng L, Xiaoyun Z, Haixia Z, Ruisheng Z, Mancang L, Zhide H, Botao F. Prediction of standard Gibbs energies of the transfer of peptide anions from aqueous solution to nitrobenzene based on support vector machine and the heuristic method. J Comput Aided Mol Des 2006; 20:1-11. [PMID: 16622797 DOI: 10.1007/s10822-005-9031-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2005] [Accepted: 11/13/2005] [Indexed: 11/29/2022]
Abstract
Quantitative structure-property relationship (QSPR) method was performed for the prediction of the standard Gibbs energies (DeltaGtheta) of the transfer of peptide anions from aqueous solution to nitrobenzene. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the peptides. The four molecular descriptors selected by the heuristic method (HM) in COmprehensive DEscriptors for Structural and Statistical Analysis (CODESSA) were used as inputs for support vector machine (SVM) and radial basis function neural networks (RNFNN). The results obtained by the novel machine learning technique, SVM, were compared with those obtained by HM and RBFNN. The root mean squared errors (RMS) of the training, predicted and overall data sets are 2.192, 2.541 and 2.267 unit (kJ/mol) for HM, 1.604, 2.478 and 1.817 unit (kJ/mol) for RBFNN and 1.5621, 2.364 and 1.756 unit (kJ/mol) for SVM, respectively. The prediction results were in agreement with the experimental values. This paper provided a potential method for predicting the physiochemical property (DeltaGtheta) of various small peptides.
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Affiliation(s)
- Luan Feng
- Department of Chemistry, Lanzhou University, 730000, Lanzhou, China
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Liu H, Yao X, Liu M, Hu Z, Fan B. Prediction of retention in micellar electrokinetic chromatography based on molecular structural descriptors by using the heuristic method. Anal Chim Acta 2006. [DOI: 10.1016/j.aca.2005.10.074] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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22
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Shi J, Luan F, Zhang H, Liu M, Guo Q, Hu Z, Fan B. QSPR Study of Fluorescence Wavelengths (λex/λem) Based on the Heuristic Method and Radial Basis Function Neural Networks. ACTA ACUST UNITED AC 2006. [DOI: 10.1002/qsar.200510142] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Liu HX, Yao XJ, Zhang RS, Liu MC, Hu ZD, Fan BT. Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines. J Comput Aided Mol Des 2005; 19:499-508. [PMID: 16317501 DOI: 10.1007/s10822-005-9003-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2005] [Accepted: 07/06/2005] [Indexed: 11/29/2022]
Abstract
The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.
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Affiliation(s)
- H X Liu
- Department of Chemistry, Lanzhou University, 730000, Lanzhou, China
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