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Liu J, Lei X, Zhang Y, Pan Y. The prediction of molecular toxicity based on BiGRU and GraphSAGE. Comput Biol Med 2023; 153:106524. [PMID: 36623439 DOI: 10.1016/j.compbiomed.2022.106524] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/10/2022] [Accepted: 12/31/2022] [Indexed: 01/04/2023]
Abstract
The prediction of molecules toxicity properties plays an crucial role in the realm of the drug discovery, since it can swiftly screen out the expected drug moleculars. The conventional method for predicting toxicity is to use some in vivo or in vitro biological experiments in the laboratory, which can easily pose a threat significant time and financial waste and even ethical issues. Therefore, using computational approaches to predict molecular toxicity has become a common strategy in modern drug discovery. In this article, we propose a novel model named MTBG, which primarily makes use of both SMILES (Simplified molecular input line entry system) strings and graph structures of molecules to extract drug molecular feature in the field of drug molecular toxicity prediction. To verify the performance of the MTBG model, we opt the Tox21 dataset and several widely used baseline models. Experimental results demonstrate that our model can perform better than these baseline models.
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Affiliation(s)
- Jianping Liu
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
| | - Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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2
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022. [DOI: https://doi.org/10.3390/ijms23031201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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3
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022; 23:ijms23031201. [PMID: 35163123 PMCID: PMC8835262 DOI: 10.3390/ijms23031201] [Citation(s) in RCA: 4] [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: 12/30/2021] [Accepted: 01/19/2022] [Indexed: 02/05/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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Affiliation(s)
- Aleksey I. Rusanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Olga A. Dmitrieva
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Nugzar Zh. Mamardashvili
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Igor V. Tetko
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
- Helmholtz Munich, Institute of Structural Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany
- BIGCHEM GmbH, D-85716 Unterschleißheim, Germany
- Correspondence: ; Tel.: +49-89-3187-3575
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4
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022. [DOI: https:/doi.org/10.3390/ijms23031201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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5
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Buglak AA, Charisiadis A, Sheehan A, Kingsbury CJ, Senge MO, Filatov MA. Quantitative Structure-Property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers*. Chemistry 2021; 27:9934-9947. [PMID: 33876842 PMCID: PMC8362084 DOI: 10.1002/chem.202100922] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Indexed: 12/30/2022]
Abstract
Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. In this work, the available data on singlet oxygen generation quantum yields (ΦΔ ) for a dataset containing >70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran) were analyzed. In order to build reliable QSPR model, a series of new BODIPYs were synthesized that bear different electron donating aryl groups in the meso position, their optical and structural properties were studied along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using density functional theory (DFT), namely M06-2X functional. QSPR models predicting ΦΔ values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R=0.88-0.91 and q2 =0.62-0.69) for all three solvents. A small root mean squared error of 8.2 % was obtained for ΦΔ values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting ΦΔ values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.
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Affiliation(s)
- Andrey A. Buglak
- Faculty of PhysicsSaint-Petersburg State UniversityUniversiteteskaya Emb. 7–9199034St. PetersburgRussia
| | - Asterios Charisiadis
- Chair of Organic Chemistry School of Chemistry Trinity Biomedical Sciences InstituteTrinity College Dublin The University of Dublin152-160Pearse StreetDublin 2Ireland
| | - Aimee Sheehan
- School of Chemical and Pharmaceutical SciencesTechnological University DublinCity Campus, Kevin StreetDublin 8Ireland
| | - Christopher J. Kingsbury
- Chair of Organic Chemistry School of Chemistry Trinity Biomedical Sciences InstituteTrinity College Dublin The University of Dublin152-160Pearse StreetDublin 2Ireland
| | - Mathias O. Senge
- Institute for Advanced Study (TUM-IAS)Technical University of MunichLichtenberg-Str. 2a85748GarchingGermany
| | - Mikhail A. Filatov
- School of Chemical and Pharmaceutical SciencesTechnological University DublinCity Campus, Kevin StreetDublin 8Ireland
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6
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Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions. J Fluoresc 2018; 28:695-706. [DOI: 10.1007/s10895-018-2233-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 04/11/2018] [Indexed: 10/17/2022]
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7
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Samari F, Yousefinejad S. Quantitative structural modeling on the wavelength interval (Δ λ ) in synchronous fluorescence spectroscopy. J Mol Struct 2017. [DOI: 10.1016/j.molstruc.2017.07.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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8
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Beheshti A, Riahi S, Ganjali MR, Norouzi P. Highlighting and trying to overcome a serious drawback with qspr studies; data collection in different experimental conditions (mixed-QSPR). J Comput Chem 2012; 33:732-47. [DOI: 10.1002/jcc.22892] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2010] [Revised: 10/03/2011] [Accepted: 10/18/2011] [Indexed: 11/08/2022]
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9
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QSAR study of some CCR5 antagonists as anti-HIV agents using radial basis function neural network and general regression neural network on the basis of principal components. Med Chem Res 2011. [DOI: 10.1007/s00044-011-9863-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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10
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Saghaie L, Shahlaei M, Madadkar-Sobhani A, Fassihi A. Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: Study of cyclin dependent kinase 4 inhibitors. J Mol Graph Model 2010; 29:518-28. [DOI: 10.1016/j.jmgm.2010.10.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2010] [Revised: 09/25/2010] [Accepted: 10/04/2010] [Indexed: 11/16/2022]
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Schüller A, Goh GB, Kim H, Lee JS, Chang YT. Quantitative Structure-Fluorescence Property Relationship Analysis of a Large BODIPY Library. Mol Inform 2010; 29:717-29. [DOI: 10.1002/minf.201000089] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2010] [Accepted: 09/28/2010] [Indexed: 12/31/2022]
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12
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Xu J, Zhang H, Wang L, Liang G, Wang L, Shen X, Xu W. QSPR study of absorption maxima of organic dyes for dye-sensitized solar cells based on 3D descriptors. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2010; 76:239-247. [PMID: 20381412 DOI: 10.1016/j.saa.2010.03.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2010] [Revised: 03/09/2010] [Accepted: 03/16/2010] [Indexed: 05/29/2023]
Abstract
A quantitative structure-property relationship (QSPR) study was performed for the prediction of the absorption maxima (lambda(max)) of organic dyes for dye-sensitized solar cells (DSSCs). The entire set of 70 dyes was divided into a training set of 53 dyes and a test set of 17 dyes according to Kennard and Stones algorithm. Three-dimensional (3D) descriptors were calculated to represent the dye molecules. A ten-descriptor model, with a squared correlation coefficient (R(2)) of 0.9543 and a standard error of estimation (s) of 14.7 nm, was produced by using the stepwise multilinear regression analysis (MLRA) on the training set. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-one-out cross-validation procedure, randomization tests, and validation through the external test set. All descriptors involved in the model were derived solely from the chemical structure of the dye molecules, which makes the model very useful to estimate the lambda(max) of dyes before they are actually synthesized.
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Affiliation(s)
- Jie Xu
- Key Lab of Green Processing & Functional Textiles of New Textile Materials, Ministry of Education, Wuhan University of Science & Engineering, No. 1, Fangzhi Road, Hongshan District, 430073 Wuhan, China.
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13
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MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives. Eur J Med Chem 2010; 45:1352-8. [DOI: 10.1016/j.ejmech.2009.12.028] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2009] [Revised: 12/09/2009] [Accepted: 12/14/2009] [Indexed: 11/18/2022]
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14
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Riahi S, Beheshti A, Ganjali MR, Norouzi P. Quantum chemical calculations to reveal the relationship between the chemical structure and the fluorescence characteristics of phenylquinolinylethynes and phenylisoquinolinylethynes derivatives, and to predict their relative fluorescence intensity. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2009; 74:1077-1083. [PMID: 19854100 DOI: 10.1016/j.saa.2009.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2008] [Revised: 08/16/2009] [Accepted: 09/12/2009] [Indexed: 05/28/2023]
Abstract
In this paper the relationship between the chemical structure and fluorescence characteristics of 30 phenylquinolinylethyne (PhQE), and phenylisoquinolinylethyne (PhIE) derivatives compounds employing ab initio calculations have been elucidated. Quantum chemical calculations (6-31G) were carried out to obtain: the optimized geometry, energy levels, charges and dipole moments of these compounds, in the singlet (steady and excited states) and triplet states. The relationship between quantum chemical descriptors, and wavelength of maximum excitation and emission indicated that these two parameters have the most correlation with quantum chemical hardness (eta). Also, stokes shift has the most correlation with the square of difference between the maximum of positive charges in the singlet steady and singlet excited states. The quantitative structure-property relationship (QSPR) of PhQE and PhIE was studied for relative fluorescence intensity (RFI). The genetic algorithm (GA) was applied to select the variables that resulted in the best-fit models. After the variable selection, multiple linear regression (MLR) and support vector machine (SVM) were both utilized to construct linear and non-linear QSPR models, respectively. The SVM model demonstrated a better performance than that of the MLR model. The route mean square error (RMSE) in the training and the test sets for the SVM model was 0.195 and 0.324, and the correlation coefficients were 0.965 and 0.960, respectively, thus revealing the reliability of this model. The resulting data indicated that SVM could be used as a powerful modeling tool for QSPR studies. According to the best of our knowledge, this is the first research on QSPR studies to predict RFI for a series of PhQE and PhIE derivative compounds using SVM.
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Affiliation(s)
- Siavash Riahi
- Institute of Petroleum Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365-4563, Tehran, Iran.
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Ji C, Li Y, Su L, Zhang X, Chen X. Quantitative structure-retention relationships for mycotoxins and fungal metabolites in LC-MS/MS. J Sep Sci 2009; 32:3967-79. [DOI: 10.1002/jssc.200900441] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Current mathematical methods used in QSAR/QSPR studies. Int J Mol Sci 2009; 10:1978-1998. [PMID: 19564933 PMCID: PMC2695261 DOI: 10.3390/ijms10051978] [Citation(s) in RCA: 126] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2009] [Accepted: 04/28/2009] [Indexed: 02/07/2023] Open
Abstract
This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.
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Quantitative structure-property relationship study on the determination of binding constant by fluorescence quenching. OPEN CHEM 2009. [DOI: 10.2478/s11532-008-0095-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
AbstractModels to predict binding constant (logK) to bovine serum albumin (BSA) should be very useful in the pharmaceutical industry to help speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We present here an extensive list of logK binding constants for thirty-five compounds to BSA determined by florescence quenching from the literature. These data have allowed us the derivation of a quantitative structure-property relationship (QSPR) model to predict binding constants to BSA of compounds on the basis of their structure. A stepwise multiple linear regression (MLR) was performed to build the model. The statistical parameter provided by the MLR model (R = 0.9200, RMS = 0.3305) indicated satisfactory stability and predictive ability for the model. Using florescence quenching spectroscopy, we also experimentally determined the binding constants to BSA for two bioactive components in traditional Chinese medicines. Using the proposed model it was possible to predict the binding constants for each, which were in good agreement with the experimental results. This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-protein interactions, and be useful in predicting the binding constants of other compounds.
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Gong Z, Zhang R, Xia B, Hu R, Fan B. Study of Nematic Transition Temperatures in Themotropic Liquid Crystal Using Heuristic Method and Radial Basis Function Neural Networks and Support Vector Machine. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200860027] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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19
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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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20
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Xu J, Xiong Q, Chen B, Wang L, Liu L, Xu W. Modeling the Relative Fluorescence Intensity Ratio of Eu(III) Complex in Different Solvents Based on QSPR Method. J Fluoresc 2008; 19:203-9. [DOI: 10.1007/s10895-008-0403-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2008] [Accepted: 07/14/2008] [Indexed: 10/21/2022]
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21
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Quantitative structure–activity relationship studies of a series of non-benzodiazepine structural ligands binding to benzodiazepine receptor. Eur J Med Chem 2008; 43:1489-98. [DOI: 10.1016/j.ejmech.2007.09.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2007] [Revised: 07/30/2007] [Accepted: 09/06/2007] [Indexed: 11/18/2022]
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22
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Modeling the excitation wavelengths (lambda(ex)) of boronic acids. J Mol Model 2008; 14:441-9. [PMID: 18351403 DOI: 10.1007/s00894-008-0293-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2007] [Accepted: 02/18/2008] [Indexed: 10/22/2022]
Abstract
The quantitative structure-property relationship (QSPR) method was used to model the fluorescence excitation wavelengths (lambda(ex)) of 42 boronic acid-based fluorescent biosensors (30 in the training set and 12 in the test set). In this QSPR study, unsupervised forward selection (UFS), stepwise multiple linear regression (SMLR), partial least squares regression (PLS) and associative neural networks (ASNN) were employed to simulate linear and nonlinear models. All models were validated by a test set and Tropsha's validation model. The resulting ASNN nonlinear model demonstrates significant improvement on the predictive ability of the neural network compared to the SMLR and PLS linear models. The descriptors used in the models are discussed in detail. These QSPR models are useful tools for the prediction of fluorescence excitation wavelengths of arylboronic acids.
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23
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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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