Optimization separation of five phosphoamidothioate enantiomers using a series of silica and chiral column by high-performance liquid chromatography

A computer-assisted method is described for optimization of multi-component,mobile phase selection for separation of five phosphoamidothioate enantiomers with a series of silica and chiral columns in normal phase HPLC.The method is based on the triangular solvent selection concept using a statistical scanning method.The optimization of the separation over the experimental region is based on a special polynomial estimation from seven experimental runs,and resolution (Rs) is used as the selection criterion.Excellent agreement was obtained between predicted and experimental data.     

机器学习方法用于二氢叶酸还原酶抑制剂的活性预测

分别采用支持向量学习机、人工神经网络、调节性逻辑回归和K-最临近等机器学习方法对761个二氢叶酸还原酶抑制剂建立了其活性分类预测模型. 采用组成描述符和拓扑描述符表征抑制剂的分子结构及物理化学性质, 使用Kennard-Stone方法进行训练集的设计, 并用Metropolis Monte Carlo模拟退火方法作变量选择. 结果表明, 支持向量学习机优于其它机器学习方法, 所得到的最优模型具有较好的预测结果, 其预测正确率为91.62%. 说明通过合适的训练集设计及变量选择, 支持向量学习机方法可以很好地用于二氢叶酸还原酶抑制剂的活性分类预测.     

The use of polymer heteronuclei for crystalline polymorph selection

A method for the production of crystalline polymorphs from solution is described which utilizes a diverse set of polymer heteronuclei. Application to crystalline polymorph selection for the important pharmaceuticals acetaminophen and carbamazepine is demonstrated. This method provides a new paradigm for polymorph selection, where solvent and temperature conditions can be chosen on the basis of process considerations and the polymer heteronucleus can be varied for specific polymorph production.     

Multiple automatic base selection: Protein–ligand docking based on incremental construction without manual intervention

A possible way of tackling the molecular docking problem arising in computer- aided drug design is the use of the incremental construction method. This method consists of three steps: the selection of a part of a molecule, a so- called base fragment, the placement of the base fragment into the active site of a protein, and the subsequent reconstruction of the complete drug molecule. Assuming that a part of a drug molecule is known, which is specific enough to be a good base fragment, the method is proven to be successful for a large set of docking examples. In addition, it leads to the fastest algorithms for flexible docking published so far. In most real-world applications of docking, large sets of ligands have to be tested for affinity to a given protein. Thus, manual selection of a base fragment is not practical. On the other hand, the selection of a base fragment is critical in that only few selections lead to a low-energy structure. We overcome this limitation by selecting a representative set of base fragments instead of a single one. In this paper, we present a set of rules and algorithms to automate this selection. In addition, we extend the incremental construction method to deal with multiple fragmentations of the drug molecule. Our results show that with multiple automated base selection, the quality of the docking predictions is almost as good as with one manually preselected base fragment. In addition, the set of solutions is more diverse and alternative binding modes with low scores are found. Although the run time of the overall algorithm increases, the method remains fast enough to search through large ligand data sets.     

Optimization of multicomponent solvent selection in high-performance liquid chromatography using a statistical method

A computer-assisted method is presented for optimization of multicomponent solvent mobile phase selection for separation of O-ethyl-N-isopropyl phosphoro (thioureido) thioates in reversed-phase HPLC and four geometric isomers of pesticides Decis in normal-phase HPLC. The method is based on Snyder's solvent selection triangle concept using a statistical method. The optimization of the separation over the experimental region is based on a special polynomial estimation from seven experimental runs, and resolution (R_s) is used as the selection criterion. Excellent agreement was obtained between predicted data and experimental results.     

Optimized separation of pesticide enantiomers by chiral high-performance liquid chromatography

Summary A computer-assisted method is described for optimization of multi-component, mobile phase selection for separating enantiomers of four pesticides in normal-phase HPLC. The method is based on the triangle, solvent-selection concept using a statistical scanning method. The optimization of the separation over the experimental region is based on a special polynomial estimation from seven experimental runs, and resolution (R_s) is used as the selection criterion. Excellent agreement was obtained between predicted and experimental data.     

Information-theoretic approaches to SVM feature selection for metagenome read classification

Analysis of DNA sequences isolated directly from the environment, known as metagenomics, produces a large quantity of genome fragments that need to be classified into specific taxa. Most composition-based classification methods use all features instead of a subset of features that may maximize classifier accuracy. We show that feature selection methods can boost performance of taxonomic classifiers. This work proposes three different filter-based feature selection methods that stem from information theory: (1) a technique that combines Kullback-Leibler, Mutual Information, and distance information, (2) a text mining technique, TF-IDF, and (3) minimum redundancy-maximum-relevance (mRMR). The feature selection methods are compared by how well they improve support vector machine classification of genomic reads. Overall, the 6mer mRMR method performs well, especially on the phyla-level. If the number of total features is very large, feature selection becomes difficult because a small subset of features that captures a majority of the data variance is less likely to exist. Therefore, we conclude that there is a trade-off between feature set size and feature selection method to optimize classification performance. For larger feature set sizes, TF-IDF works better for finer-resolutions while mRMR performs the best out of any method for N=6 for all taxonomic levels.     

Nonequilibrium capillary electrophoresis of equilibrium mixtures: a universal tool for development of aptamers

Aptamers are DNA (or RNA) ligands selected from large libraries of random DNA sequences and capable of binding different classes of targets with high affinity and selectivity. Both the chances for the aptamer to be selected and the quality of the selected aptamer are largely dependent on the method of selection. Here we introduce selection of aptamers by nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM). The new method has a number of advantages over conventional approaches. First, NECEEM-based selection has exceptionally high efficiency, which allows aptamer development with fewer rounds of selection. Second, NECEEM can be equally used for selecting aptamers and finding their binding parameters. Finally, due to its comprehensive kinetic capabilities, the new method can potentially facilitate selection of aptamers with predefined K(d), k(off), and k(on) of the aptamer-target interaction. In this proof-of-principle work, we describe the theoretical bases of the method and demonstrate its application to a one-step selection of DNA aptamers with nanomolar affinity for protein farnesyltransferase.     

变量选择中的正交变换法及苯酚和苯胺类衍生物色谱比移值的预测

正交变换法是变量选择的一种可行方法,但该种方法非常依赖于正交变换过程中变量的排序,侧重比较了不同排序方法,其中,后退法可以得到较好的结果。文中采用此种方法对由苯酚及苯胺类化合物所衍生的变量进行了正交变换,并对上述化合物的色谱比移值进行了预测。同时,与前进选择法、后退剔除法和逐步回归法几种传统方法进行了比较,得到了有启示性的结果。     

ESI-FTICR mass spectrometry employing Data-dependent external ion selection and accumulation

Data-dependent external m/z selection and accumulation of ions is demonstrated in use with ESI-FTICR instrumentation, with two different methods for ion selection being explored. One method uses RF/DC quadrupole filtering and is described in use with an 11.5 tesla (T) FTICR instrument, while the second method employs RF-only resonance dipolar excitation selection and is described in use with a 3.5 T FTICR instrument. In both methods ions are data-dependently selected on the fly in a linear quadrupole ion guide, then accumulated in a second linear RF-only quadrupole trap that immediately follows. A major benefit of ion preselection prior to external accumulation is the enhancement of ion populations for low-level species. This development is expected to expand the dynamic range and sensitivity of FTICR for applications including analysis of complex polypeptide mixtures (e.g., proteomics).     

A novel divide-and-merge classification for high dimensional datasets

High dimensional datasets contain up to thousands of features, and can result in immense computational costs for classification tasks. Therefore, these datasets need a feature selection step before the classification process. The main idea behind feature selection is to choose a useful subset of features to significantly improve the comprehensibility of a classifier and maximize the performance of a classification algorithm. In this paper, we propose a one-per-class model for high dimensional datasets. In the proposed method, we extract different feature subsets for each class in a dataset and apply the classification process on the multiple feature subsets. Finally, we merge the prediction results of the feature subsets and determine the final class label of an unknown instance data. The originality of the proposed model is to use appropriate feature subsets for each class. To show the usefulness of the proposed approach, we have developed an application method following the proposed model. From our results, we confirm that our method produces higher classification accuracy than previous novel feature selection and classification methods.     

Improved binary PSO for feature selection using gene expression data

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved, available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. A reliable selection method for genes relevant for sample classification is needed in order to speed up the processing rate, decrease the predictive error rate, and to avoid incomprehensibility due to the large number of genes investigated. Improved binary particle swarm optimization (IBPSO) is used in this study to implement feature selection, and the K-nearest neighbor (K-NN) method serves as an evaluator of the IBPSO for gene expression data classification problems. Experimental results show that this method effectively simplifies feature selection and reduces the total number of features needed. The classification accuracy obtained by the proposed method has the highest classification accuracy in nine of the 11 gene expression data test problems, and is comparative to the classification accuracy of the two other test problems, as compared to the best results previously published.     

Dissimilar or orthogonal reversed-phase chromatographic systems: A comparison of selection techniques

Developing an analytical separation procedure for an unknown mixture is a challenging issue. An important example is the separation and quantification of a new drug and its impurities. One approach to start method development is the screening of the mixture on dissimilar chromatographic systems, i.e. systems with large selectivity differences. After screening, the most suited system is retained for further method development. In a step prior to such strategy dissimilar chromatographic systems need to be selected. In this paper the performance of different chemometric selection approaches, described in the literature, was visually evaluated and compared. Additionally, orthogonal projection approach (OPA) was tested as another potential selection method. All techniques, including the OPA method, were able to select (a set of) dissimilar chromatographic systems and many similarities between the selections were observed. However, the Kennard and Stone algorithm performed best in selecting the most dissimilar systems in the earliest steps of the selection procedure. The generalized pairwise correlation method (GPCM) and the auto-associative multivariate regression trees (AAMRT) were also performing well. OPA and weighted pair group method using arithmetic averages (WPGMA) are less preferable.     

Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.     

Monitoring the progression of the in vitro selection of nucleic acid aptamers by denaturing high-performance liquid chromatography

Monitoring of in-vitro selection experiments is crucial in evaluation of the success and outcome of such approaches. Furthermore, monitoring running in parallel with the selection procedure enables early intervention and adjustment of stringency to achieve the desired activities of the selected nucleic acid species. Here we describe the use of a non-radioactive method that enables monitoring of a SELEX procedure on the basis of sequence diversity. We employ denaturing HPLC and describe for the first time an experimental set-up that is useful both for analysis of the progression of in-vitro selection experiments and for separation of distinct aptamer sequences.     

Variable interaction network based variable selection for multivariate calibration

Multivariate calibration problems often involve the identification of a meaningful subset of variables, from a vast number of variables for better prediction of output variables. A new graph theoretic method based on partial correlations (variable interaction network—VIN) is proposed. Many well studied representative calibration datasets spanning different application domains are selected for investigating the performance. Partial least squares (PLS) regression models combined with variable selection techniques are employed for benchmarking the performance. Subsets of variables with different number of variables are retained for the final analysis after VIN selection and progressive prediction accuracies are used for comparison. VIN-PLS results show significant improvement in prediction efficiencies and variable subset optimization. Improvement of up to 45% over existing methods with significantly fewer variables is achieved using the new method. Advantages of VIN based variable selection are highlighted.     

正交递归选择法及其应用

本文提出一种新的变量筛选法－正交递归选择法，该法可以得到预报能力较强的模型，即ＰＲＥＳＳ（预报残差平方和）值较低的模型。用该法处理构效关系问题，并与逐步回归正向选择法及ＰＬＳ回归法进行了比较，得到满意的结果。