The Experts below are selected from a list of 1329 Experts worldwide ranked by ideXlab platform
Dongsheng Cao - One of the best experts on this subject based on the ideXlab platform.
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adme properties evaluation in drug discovery prediction of caco 2 cell permeability using a combination of nsga ii and boosting
Journal of Chemical Information and Modeling, 2016Co-Authors: Ningning Wang, Jie Dong, Yinhua Deng, Minfeng Zhu, Ming Wen, Zhijiang Yao, Jianbing Wang, Dongsheng CaoAbstract:The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure-property relationship (QSPR) Study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with R(2) = 0.97, RMSEF = 0.12, Q(2) = 0.83, RMSECV = 0.31 for the training set and RT(2) = 0.81, RMSET = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development.
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ADME Properties Evaluation in Drug Discovery: Prediction of Caco‑2 Cell Permeability Using a Combination of NSGA-II and Boosting
2016Co-Authors: Ningning Wang, Jie Dong, Yinhua Deng, Minfeng Zhu, Ming Wen, Zhijiang Yao, Jianbing Wang, Dongsheng CaoAbstract:The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure–property relationship (QSPR) Study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with R2 = 0.97, RMSEF = 0.12, Q2 = 0.83, RMSECV = 0.31 for the training set and RT2 = 0.81, RMSET = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development
Feng Luan - One of the best experts on this subject based on the ideXlab platform.
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matrix trace operators from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions micelle nanoparticles and drug adme processes
Current Drug Metabolism, 2014Co-Authors: Humberto Gonzalezdiaz, Sonia Arrasate, Asier Gomezsan Juan, Nuria Sotomayor, Esther Lete, Alejandro Speckplanche, Juan M Ruso, Feng Luan, Maria Natalia Dias Soeiro CordeiroAbstract:The Study of quantitative structure-property relationships (QSPR) is important to Study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR Study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.
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quantitative structure property relationship Study for the prediction of characteristic infrared absorption of carbonyl group of commonly used carbonyl compounds
Vibrational Spectroscopy, 2011Co-Authors: Huitao Liu, Yingying Wen, Feng LuanAbstract:Abstract Quantitative structure–property relationship (QSPR) Study was applied to the prediction of the characteristic infrared absorption frequency of carbonyl group ( ν C O ) of 65 commonly used carbonyl compounds using linear heuristic method (HM) and non-linear radial basis function neural network (RBFNN) based on their structures alone. HM was used both for pre-selecting molecular descriptors and for developing the linear model. The statistical parameters provided by the HM model were R 2 = 0.873, R CV 2 = 0.838 , F = 67.348, and RMS = 16.267. The five molecular descriptors selected by HM method were used as inputs for RBFNN to establish the non-linear model. The RBFNN model's results were: R 2 = 0.943, R CV 2 = 0 .911 , F = 880.885, and RMS = 10.310. The proposed models were evaluated for predictive ability with an external validation set, and the statistical parameters obtained were R EXT 2 = 0.876 , F = 56.732, RMS = 19.754 for HM and R EXT 2 = 0 .908 , F = 79.010, RMS = 13.748 for RBFNN. The results indicate that the simple linear model can be used to predict ν C O of carbonyl compounds, while the non-linear model can give more accurate results.
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QSPR Study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network
Open Chemistry, 2009Co-Authors: Huitao Liu, Feng Luan, Yingying Wen, Yuan GaoAbstract:The half-wave potential (E1/2) is an important electrochemical property of organic compounds. In this work, a quantitative structure-property relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E1/2) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R2=0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E1/2, and can be useful in predicting the E1/2 of other compounds.
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QSPR Study of permeability coefficients through low density polyethylene based on radial basis function neural networks and the heuristic method
Computational Materials Science, 2006Co-Authors: Feng Luan, Haixia Zhang, Mancang Liu, Xin Zhang, Rudong Zhang, Botao FanAbstract:Abstract Traditional quantitative structure–permeability relationship (QSPR) is performed for the Study of permeability coefficients of various compounds through low-density polyethylene at 21.1 °C. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the compounds. The three molecular descriptors selected by the heuristic method (HM) in CODESSA were used as inputs for radial basis function neural networks (RBFNNs). The results obtained by RBFNNs were compared with those by HM. The root-mean-squared errors (RMS) for the whole data set given by HM and RBFNNs were 0.4565 and 0.3461, respectively, which shows the RBFNNs model is better than the HM model. The prediction results are in agreement with the experimental values. This paper provided a potential method for predicting the permeability coefficient in polymer science.
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QSPR Study of fluorescence wavelengths λex λem based on the heuristic method and radial basis function neural networks
Qsar & Combinatorial Science, 2006Co-Authors: Jing Shi, Feng Luan, Haixia Zhang, Mancang Liu, Qingxiang Guo, Botao FanAbstract:The Quantitative Structure-Property Relationship (QSPR) method was performed to Study the fluorescence excitation wavelengths (lambda(ex)) and emission wavelengths (lambda(em)) of 64 fluorescent probes. The probes included the derivatives of dansyl, bimane, pyrene, benzofurazan, nonaphthalene, coumarin, anthracene and fluorescein. with the wavelength ranging from 300 nm to 600 nm. The Heuristic Method (HM) and Radial Basis Function Neural Networks (RBFNNs) were employed to construct linear and nonlinear prediction models, respectively. The proposed linear models for lambda(ex) and lambda(em) contain five descriptors with the squared correlation coefficients R-2 of 0.888 and 0.897, respectively Better prediction results were obtained from RBFNN model, with the squared correlation coefficients R-2 of 0.948 and 0.939 for lambda(ex) and lambda(em) respectively. The descriptors used in the models were discussed in detail too.
Melody Yekta - One of the best experts on this subject based on the ideXlab platform.
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quantitative structure property relationship Study of standard formation enthalpies of acyclic alkanes using atom type based ai topological indices
Arabian Journal of Chemistry, 2017Co-Authors: Fariba Safa, Melody YektaAbstract:Abstract A quantitative structure–property relationship (QSPR) Study was performed for prediction of enthalpies of 134 acyclic alkanes using modified Xu ( m Xu) index and atom-type-based AI topological indices. At first, a simple linear regression model was developed using m Xu index alone and the statistics were R 2 = 0.947, F = 2335 and standard error of 1.00. The results showed that combination of the atom-type-based AI topological indices and m Xu index can produce significant improvement in the statistical quality of the model, especially the decrease in the standard error was 33% relative to the simple linear model. The final model was validated to be statistically significant and reliable using external validation technique. External validation was performed by dividing the entire data set into three subsets and predicting enthalpy values for each subset from the other two as training sets. Average standard error of calibration of 0.66 and average standard error of prediction of 0.68 demonstrated the validity and good efficiency of the topological indices in modeling enthalpies of alkanes. The obtained results showed that the enthalpy for acyclic alkanes is dominated by molecular size and the atomic groups are also important although their contributions are much smaller than that of the molecular size.
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Quantitative structure–property relationship Study of standard formation enthalpies of acyclic alkanes using atom-type-based AI topological indices
Elsevier, 2017Co-Authors: Fariba Safa, Melody YektaAbstract:A quantitative structure–property relationship (QSPR) Study was performed for prediction of enthalpies of 134 acyclic alkanes using modified Xu (mXu) index and atom-type-based AI topological indices. At first, a simple linear regression model was developed using mXu index alone and the statistics were R2 = 0.947, F = 2335 and standard error of 1.00. The results showed that combination of the atom-type-based AI topological indices and mXu index can produce significant improvement in the statistical quality of the model, especially the decrease in the standard error was 33% relative to the simple linear model. The final model was validated to be statistically significant and reliable using external validation technique. External validation was performed by dividing the entire data set into three subsets and predicting enthalpy values for each subset from the other two as training sets. Average standard error of calibration of 0.66 and average standard error of prediction of 0.68 demonstrated the validity and good efficiency of the topological indices in modeling enthalpies of alkanes. The obtained results showed that the enthalpy for acyclic alkanes is dominated by molecular size and the atomic groups are also important although their contributions are much smaller than that of the molecular size
Botao Fan - One of the best experts on this subject based on the ideXlab platform.
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QSPR Study of permeability coefficients through low density polyethylene based on radial basis function neural networks and the heuristic method
Computational Materials Science, 2006Co-Authors: Feng Luan, Haixia Zhang, Mancang Liu, Xin Zhang, Rudong Zhang, Botao FanAbstract:Abstract Traditional quantitative structure–permeability relationship (QSPR) is performed for the Study of permeability coefficients of various compounds through low-density polyethylene at 21.1 °C. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the compounds. The three molecular descriptors selected by the heuristic method (HM) in CODESSA were used as inputs for radial basis function neural networks (RBFNNs). The results obtained by RBFNNs were compared with those by HM. The root-mean-squared errors (RMS) for the whole data set given by HM and RBFNNs were 0.4565 and 0.3461, respectively, which shows the RBFNNs model is better than the HM model. The prediction results are in agreement with the experimental values. This paper provided a potential method for predicting the permeability coefficient in polymer science.
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QSPR Study of fluorescence wavelengths λex λem based on the heuristic method and radial basis function neural networks
Qsar & Combinatorial Science, 2006Co-Authors: Jing Shi, Feng Luan, Haixia Zhang, Mancang Liu, Qingxiang Guo, Botao FanAbstract:The Quantitative Structure-Property Relationship (QSPR) method was performed to Study the fluorescence excitation wavelengths (lambda(ex)) and emission wavelengths (lambda(em)) of 64 fluorescent probes. The probes included the derivatives of dansyl, bimane, pyrene, benzofurazan, nonaphthalene, coumarin, anthracene and fluorescein. with the wavelength ranging from 300 nm to 600 nm. The Heuristic Method (HM) and Radial Basis Function Neural Networks (RBFNNs) were employed to construct linear and nonlinear prediction models, respectively. The proposed linear models for lambda(ex) and lambda(em) contain five descriptors with the squared correlation coefficients R-2 of 0.888 and 0.897, respectively Better prediction results were obtained from RBFNN model, with the squared correlation coefficients R-2 of 0.948 and 0.939 for lambda(ex) and lambda(em) respectively. The descriptors used in the models were discussed in detail too.
Fariba Safa - One of the best experts on this subject based on the ideXlab platform.
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quantitative structure property relationship Study of standard formation enthalpies of acyclic alkanes using atom type based ai topological indices
Arabian Journal of Chemistry, 2017Co-Authors: Fariba Safa, Melody YektaAbstract:Abstract A quantitative structure–property relationship (QSPR) Study was performed for prediction of enthalpies of 134 acyclic alkanes using modified Xu ( m Xu) index and atom-type-based AI topological indices. At first, a simple linear regression model was developed using m Xu index alone and the statistics were R 2 = 0.947, F = 2335 and standard error of 1.00. The results showed that combination of the atom-type-based AI topological indices and m Xu index can produce significant improvement in the statistical quality of the model, especially the decrease in the standard error was 33% relative to the simple linear model. The final model was validated to be statistically significant and reliable using external validation technique. External validation was performed by dividing the entire data set into three subsets and predicting enthalpy values for each subset from the other two as training sets. Average standard error of calibration of 0.66 and average standard error of prediction of 0.68 demonstrated the validity and good efficiency of the topological indices in modeling enthalpies of alkanes. The obtained results showed that the enthalpy for acyclic alkanes is dominated by molecular size and the atomic groups are also important although their contributions are much smaller than that of the molecular size.
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Quantitative structure–property relationship Study of standard formation enthalpies of acyclic alkanes using atom-type-based AI topological indices
Elsevier, 2017Co-Authors: Fariba Safa, Melody YektaAbstract:A quantitative structure–property relationship (QSPR) Study was performed for prediction of enthalpies of 134 acyclic alkanes using modified Xu (mXu) index and atom-type-based AI topological indices. At first, a simple linear regression model was developed using mXu index alone and the statistics were R2 = 0.947, F = 2335 and standard error of 1.00. The results showed that combination of the atom-type-based AI topological indices and mXu index can produce significant improvement in the statistical quality of the model, especially the decrease in the standard error was 33% relative to the simple linear model. The final model was validated to be statistically significant and reliable using external validation technique. External validation was performed by dividing the entire data set into three subsets and predicting enthalpy values for each subset from the other two as training sets. Average standard error of calibration of 0.66 and average standard error of prediction of 0.68 demonstrated the validity and good efficiency of the topological indices in modeling enthalpies of alkanes. The obtained results showed that the enthalpy for acyclic alkanes is dominated by molecular size and the atomic groups are also important although their contributions are much smaller than that of the molecular size
