Mancang Liu

Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression.

Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one involves an application of SVM to the development of a QSAR model for the prediction of toxicities of 153 phenols, and the second investigation deals with the QSAR model between the structures and the activities of a set of 85 cyclooxygenase 2 (COX-2) inhibitors. For each application, the molecular structures were described using either the physicochemical parameters or molecular descriptors. In both studied cases, the predictive ability of the SVM model is comparable or superior to those obtained by MLR and RBFNN. The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies.

A novel sol-gel-material prepared by a surface imprinting technique for the selective solid-phase extraction of bisphenol A

A novel and simple imprinted amino-functionalized silica gel material was synthesized by combining a surface molecular imprinting technique with a sol-gel process on the supporter of activated silica gel for solid-phase extraction-high performance liquid chromatography (SPE-HPLC) determination of bisphenol A (BPA). Non-imprinted silica sorbent was synthesized without the addition of BPA using the same procedure as that of BPA-imprinted silica sorbent. The BPA-imprinted silica sorbent and non-imprinted silica sorbent were characterized by FT-IR and the static adsorption experiments. The prepared BPA-imprinted silica sorbent showed high adsorption capacity, significant selectivity and good site accessibility for BPA. The maximum static adsorption capacity of the BPA-imprinted and non-imprinted silica sorbent for BPA was 68.9 and 34.0mgg(-1), respectively. The relatively selective factor value of this BPA-imprinted silica sorbent was 4.5. Furthermore, the difference of the retention characteristics of BPA on the C(8) SPE column and BPA-imprinted silica SPE (MIP-SPE) was compared. The MIP-SPE-HPLC method showed higher selectivity to BPA than the traditional SPE-HPLC method. At last, the BPA-imprinted polymers were used as the sorbent in solid-phase extraction to determine BPA in water samples with satisfactory recovery higher than 99% (R.S.D. 3.7%).

Diagnosing breast cancer based on support vector machines.

The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.

Radial basis function neural network-based QSPR for the prediction of critical temperature

Abstract A QSPR study was performed to develop models that relate the structures of 856 organic compounds to their critical temperatures. Molecular descriptors derived solely from structure were used to represent molecular structures. A subset of the calculated descriptors selected using forward stepwise regression was used in the QSPR models development. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) are utilized to construct the linear and nonlinear QSPR models, respectively. The optimal QSPR model was developed based on a 10–33–1 radial basis function neural network architecture using molecular descriptors calculated from molecular structure alone. The root mean square errors in critical temperature predictions were 13.97 K for the whole set, 12.32 K for the training set, and 14.23 K for the prediction set. The prediction results are in good agreement with the experimental value.

Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs

The support vector machine (SVM), as a novel type of a learning machine, for the first time, was used to develop a QSPR model that relates the structures of 35 amino acids to their isoelectric point. Molecular descriptors calculated from the structure alone were used to represent molecular structures. The seven descriptors selected using GA-PLS, which is a sophisticated hybrid approach that combines GA as a powerful optimization method with PLS as a robust statistical method for variable selection, were used as inputs of RBFNNs and SVM to predict the isoelectric point of an amino acid. The optimal QSPR model developed was based on support vector machines, which showed the following results: the root-mean-square error of 0.2383 and the prediction correlation coefficient R=0.9702 were obtained for the whole data set. Satisfactory results indicated that the GA-PLS approach is a very effective method for variable selection, and the support vector machine is a very promising tool for the nonlinear approximation.

Selective solid-phase extraction using molecular imprinted polymer for the analysis of diethylstilbestrol.

A simple imprinted amino-functionalized silica gel material was synthesized by combining a surface molecular imprinting technique with a sol-gel process for solid-phase extraction-high performance liquid chromatography (SPE-HPLC) determination of diethylstilbestrol (DES). Activated silica gel was used as the supporter and non-imprinted silica sorbent was synthesized without the addition of DES using the same procedure as that of DES-imprinted silica sorbent. Compared with non-imprinted polymer particles, the prepared DES-imprinted silica sorbent showed high adsorption capacity, significant selectivity, good site accessibility and fast binding kinetics for DES. The maximum static adsorption capacity of the DES-imprinted and non-imprinted silica sorbent for DES was 62.58mgg(-1) and 19.89mgg(-1), respectively. The relatively selective factor value of this DES-imprinted silica sorbent was 61.7 at the level of 50mgL(-1). And the uptake kinetics was fairly rapid so that the adsorbent equilibrium was achieved within 10min. Furthermore, the DES-imprinted polymers were used as the sorbent in solid-phase extraction to determine DES in fish samples. The MIP-SPE-HPLC method showed higher selectivity and good recoveries higher than 87.5% (R.S.D. 11.6%).

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R2) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.

QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-kappa B mediated gene expression based on support vector machines.

The support vector machine, as a novel type of learning machine, for the first time, was used to develop a QSAR model of 57 analogues of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate (EPC), an inhibitor of AP-1 and NF-kappa B mediated gene expression, based on calculated quantum chemical parameters. The quantum chemical parameters involved in the model are Kier and Hall index (order3) (KHI3), Information content (order 0) (IC0), YZ Shadow (YZS) and Max partial charge for an N atom (MaxPCN), Min partial charge for an N atom (MinPCN). The mean relative error of the training set, the validation set, and the testing set is 1.35%, 1.52%, and 2.23%, respectively, and the maximum relative error is less than 5.00%.

Highly sensitive analysis of substituted aniline compounds in water samples by using oxidized multiwalled carbon nanotubes as an in-tube solid-phase microextraction medium

An automated on-line method for the determination of the substituted aniline compounds was developed using in-tube solid-phase microextraction coupling to high-performance liquid chromatography (HPLC). In this work, oxidized multiwalled carbon nanotubes (MWCNTs-COOH) coated on the outer surface of the fused-silica tube and inserted in the polyether ether ketone (PEEK) tubing, which was fixed directly on the six-port injection valve to substitute for the sample loop. The extraction procedure was performed by a constant flow pump frequently driving the sample solution through the PEEK tubing and the analytes were adsorbed onto MWCNTs-COOH materials when the six-port valve set to load position. After extraction, the valve switched to inject position and the extracted analytes were desorbed by mobile phase in dynamic mode. High extraction capacity was achieved for the investigated analytes and great improvement of the limits of detection was obtained in comparison with other methods. The calibration plots were linear (r(2)> or =0.9949) over the concentration range of 1.04-104ngmL(-1) for 4-nitroaniline, 1.02-102ngmL(-1) for 2-nitroaniline, 1.68-168ngmL(-1) for 2-chloroaniline and 1.09-109ngmL(-1) for 2,4-dichloroaniline. The detection limit ranged from 0.04ngmL(-1) to 0.13ngmL(-1) (at S/N=3). The possibility of applying the established method to water samples analysis was also studied.

A study of the interaction between a new reagent and serum albumin by fluorescence spectroscopy

Abstract The synthesis of a new reagent, saturated fatty hydrocarbon substituting group compound N-n-hexyl-N ′-(sodium p-aminobenzenesulfonate) thiourea (HXPT), is described. The interactions between HXPT and bovine serum albumin or human serum albumin were studied by fluorescence spectroscopy. The binding constants of HXPT with BSA or HSA were determined at different temperatures under the optimum conditions. The binding sites were obtained and the acting force were suggested to be mainly hydrophobic. The effect of common ions on the binding constants was also investigated. A practical method was proposed for the determination of HXPT in bovine serum or human serum samples.