Chunxia Xue

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.

An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines.

The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as inputs for the SVM model. The root-mean-square (rms) errors in BDE predictions for the training, test, and overall data sets were 3.808, 3.320, and 3.713 BDE units (kJ mol(-1)), respectively. The results obtained by Gaussian-kernel SVM were much better than those obtained by multiple linear regression, radial basis function neural networks, linear-kernel SVM, and other QSPR approaches.

Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine.

A new method support vector machine (SVM) and the heuristic method (HM) were used to develop the nonlinear and linear models between the capacity factor (logk) and seven molecular descriptors of 75 peptides for the first time. The molecular descriptors representing the structural features of the compounds only included the constitutional and topological descriptors, which can be obtained easily without optimizing the structure of the molecule. The seven molecular descriptors selected by the heuristic method in CODESSA were used as inputs for SVM. The results obtained by SVM were compared with those obtained by the heuristic method. The prediction result of the SVM model is better than that of heuristic method. For the test set, a predictive correlation coefficient R = 0.9801 and root-mean-square error of 0.1523 were obtained. The prediction results are in very good agreement with the experimental values. But the linear model of the heuristic method is easier to understand and ready to use for a chemist. This paper provided a new and effective method for predicting the chromatography retention of peptides and some insight into the structural features which are related to the capacity factor of peptides.

QSPR prediction of GC retention indices for nitrogen-containing polycyclic aromatic compounds from heuristically computed molecular descriptors

Gas chromatographic retention indices of nitrogen-containing polycyclic aromatic compounds (N-PACs) have been predicted by quantitative structure-property relationship (QSPR) analysis based on heuristic method (HM) implemented in CODESSA. In order to indicate the influence of different molecular descriptors on retention indices and well understand the important structural factors affecting the experimental values, three multivariable linear models derived from three groups of different molecular descriptors were built. Moreover, each molecular descriptor in these models was discussed to well understand the relationship between molecular structures and their retention indices. The proposed models gave the following results: the square of correlation coefficient, R(2), for the models with one, two and three molecular descriptors was 0.9571, 0.9776 and 0.9846, respectively.

Study of the quantitative structure-mobility relationship of carboxylic acids in capillary electrophoresis based on support vector machines.

The support vector machines (SVM), as a novel type of learning machine, were used to develop a quantitative structure-mobility relationship (QSMR) model of 58 aliphatic and aromatic carboxylic acids based on molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) were also utilized to construct the linear and the nonlinear model to compare with the results obtained by SVM. The root-mean-square errors in absolute mobility predictions for the whole data set given by MLR, RBFNNs, and SVM were 1.530, 1.373, and 0.888 mobility units (10(-5) cm(2) S(-1) V(-1)), respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and RBFNNs models for the prediction of the absolute mobility of carboxylic acids. Moreover, the models we proposed could also provide some insight into what structural features are related to the absolute mobility of aliphatic and aromatic carboxylic acids.

Support Vector Machines-Based Quantitative Structure−Property Relationship for the Prediction of Heat Capacity

The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to construct quantitative linear and nonlinear models to compare with the results obtained by SVM. The root-mean-square (rms) errors in heat capacity predictions for the whole data set given by MLR, RBFNNs, and SVM were 4.648, 4.337, and 2.931 heat capacity units, respectively. The prediction results are in good agreement with the experimental value of heat capacity; also, the results reveal the superiority of the SVM over MLR and RBFNNs models.

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure–activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

Diagnosing anorexia based on partial least squares, back propagation neural network, and support vector machines

Support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a predictive model for early diagnosis of anorexia. It was based on the concentration of six elements (Zn, Fe, Mg, Cu, Ca, and Mn) and the age extracted from 90 cases. Compared with the results obtained from two other classifiers, partial least squares (PLS) and back-propagation neural network (BPNN), the SVM method exhibited the best whole performance. The accuracies for the test set by PLS, BPNN, and SVM methods were 52%, 65%, and 87%, respectively. Moreover, the models we proposed could also provide some insight into what factors were related to anorexia.

The high-sensitivity determination of protein concentrations by the enhancement of Rayleigh light scattering of Arsenazo-DBN

A new Rayleigh light scattering (RLS) assay of protein is presented in this paper. At the optimum pH 4.10, the weak RLS of Arsenazo-DBN can be greatly enhanced by the addition of proteins due to the interaction between protein and Arsenazo-DBN. Based on this, the reactions of Arsenazo-DBN and proteins, including bovine serum albumin, human serum album, gamma-globulin, egg albumin, lysozyme and trypsin, were studied. A new quantitative determination method for proteins has been developed. The linear range for human serum albumin, for example, is 0.085-34.62 micrograms mL-1 with a detection limit of 44.8 ng mL-1. Besides high sensitivity, the method is characterized by good reproducibility, rapidity of reaction, good stability, and few interfering substances. The determination of the proteins in human serum and urine samples by this method give results very close to those obtained using Coomassie Brilliant Blue G-250 colorimetry, with relative standard deviations of 0.7-2.5%.

Determination of proteins at nanogram levels based on their enhancement effects of Rayleigh light scattering on dibromomethylchlorophosphonazo.

A new Rayleigh light scattering (RLS) assay of protein was conducted in this paper. At the optimum pH conditions, and in the presence of Tween-20, the weak RLS of dibromomethylchlorophosphonazo (DBM-CPA) can be enhanced greatly by the addition of proteins. Based on this, the reactions of DBM-CPA and proteins were studied. A new quantitative determination method for proteins has been developed. The method is simple, practical and relatively free from interference from coexisting substances, as well as much more sensitive (the dynamic ranges of 0.065-40.05 microg ml(-1) and detection limit of 30 ng ml(-1) for bovine serum albumin (BSA)) than most of the existing assays. The determination results of human body serum samples are identical to those by the CBB method, with relative S.D. of six determination of 0.5-2.2%.