Modelling and prediction of retention in high-performance liquid chromatography by using neural networks

Summary Multi-layer feed-forward neural networks trained with an error back-propagation algorithm have been used to model retention behaviour of liquid chromatography as a function of the composition of the mobile phases. Conventional hydro-organic and micellar mobile phases were considered. Accurate retention modelling and prediction have been achieved using mobile phases defined by two, three and four parameters. With micellar mobile phases, the parameters involved included the concentrations of surfactant and organic modifier, pH and temperature. It is shown that neural networks provide a competitive tool to model varied inherent nonlinear relationships of retention behaviour with respect to the mobile phase parameters. The soft models defined by the weights of the networks are capable of accommodating all types of linear and nonlinear relationships, neural networks being specially useful when the relationships between retention behaviour and the mobile phase parameters are unknown. However, to train neural networks more experimental points than with hard-modelling methods are required, hence the use of the networks is recommended only for those cases where adequate theoretical or empirical models do not exist.     

Modelling and prediction of retention in high-performance liquid chromatography by using neural networks

Summary Two analytical methods have been developed for the determination in water of 18 priority phenolics listed in US EPA method 604 and on EEC list 76/464. A solidphase extraction system using eight different sorbents packed in a precolumn was coupled on-line with a liquid chromatograph with UV detection. The ensuing method uses 50–100 mL of ground water; its performance was compared with that of an off-line method using Empore extraction disks and 1 L water samples. Phenol recoveries varied from <20 to 100% for concentrations in the range 0.1–10 g/L at an acid pH. The presence of the phenols in water was confirmed by using thermospray LC-mass spectrometry in the negative ion mode. The stability of the phenols in water was studied at a 10 g/l level in ground and estuarine water at acid pH (2.5–3) and at 4°C for 1 month. The system was validated by various interlaboratory exercises with samples containing 2,4,6-trichlorophenol and pentachlorophenol at concentrations from 0.1 to 0.5 g/L.     

基于支持向量机的药物诱导磷脂质病预测模型

本文应用一种组合遗传算法和共轭梯度法的支持向量机(GA-CG-SVM)方法建立了药物诱导磷脂质病分类预测模型.首先对描述符进行了优化,选出了19个描述符用于模型的构建,所建模型对训练集的预测准确率为81.6%,对测试集的预测精度为87.5%,说明所建SVM分类模型不仅能正确预测训练集药物诱导的磷脂质病,也对其他化合物具...     

Quantitative determination of celanide by high-performance liquid chromatography

Methods have been developed for the quantitative determination of celanide [lanatoside C] in substances, in solutions for injection, and in tablets by high-performance liquid chromatography, with the aid of which it is possible to determine small amounts of celanide (0.025 mg/ml) with adequate accuracy. The relative error of the determination does not exceed ±4.0%.All-Union Scientific-Research Institute of Physical Culture, Moscow. Translated from Khimiya Prirodnykh Soedinenii, No. 4, pp. 476–479, July–August, 1983.     

Quantitative analysis of Pentamax tablets by high-performance liquid chromatography

A procedure was proposed for quantitative analysis of Pentamax tablets, a new multicomponent drug, by high-performance liquid chromatography. Model mixtures containing all active and auxiliary components of the tablets were analyzed by the introduced/found method; the absence of a systematic determination error was verified. Preproduction tablet samples were analyzed. The results of the analysis match the requirements of normative technical documentation and technologic loads.     

The cross-species prediction of bacterial promoters using a support vector machine

Due to degeneracy of the observed binding sites, the in silico prediction of bacterial sigma(70)-like promoters remains a challenging problem. A large number of sigma(70)-like promoters has been biologically identified in only two species, Escherichia coli and Bacillus subtilis. In this paper we investigate the issues that arise when searching for promoters in other species using an ensemble of SVM classifiers trained on E. coli promoters. DNA sequences are represented using a tagged mismatch string kernel. The major benefit of our approach is that it does not require a prior definition of the typical -35 and -10 hexamers. This gives the SVM classifiers the freedom to discover other features relevant to the prediction of promoters. We use our approach to predict sigma(A) promoters in B. subtilis and sigma(66) promoters in Chlamydia trachomatis. We extended the analysis to identify specific regulatory features of gene sets in C. trachomatis having different expression profiles. We found a strong -35 hexamer and TGN/-10 associated with a set of early expressed genes. Our analysis highlights the advantage of using TSS-PREDICT as a starting point for predicting promoters in species where few are known.     

Fragmental descriptors in (Q)SAR: prediction of the assignment of organic compounds to pharmacological groups using the support vector machine approach

The structure—activity classification models for prediction of the assignment of organic compounds to 40 pharmacological groups were constructed in the framework of the fragmental approach using the support vector machine technique, the Platt—Wu probabilistic model, and resampling procedure. The models constructed allow one to predict possible types of the side pharmacological effects of drugs.