Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds

Goryński, Krzysztof; Bojko, Barbara; Nowaczyk, Alicja; Buciński, Adam; Pawliszyn, Janusz; Kaliszan, Roman

doi:10.1016/j.aca.2013.08.025

Cited by 87 publications

(74 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many different approaches to t R prediction exist and range from the simple (Kern et al, 2009;Nurmi et al, 2012) to the complex (Goryński et al, 2013;Ji et al, 2009;Kaliszan et al, 2003;Ukić et al, 2014a). For example, logK ow models can be derived using freely accessible data from chemical databases such as ChemSpider and PubChem, as well as freeware prediction sources such as VCCLABS.…”

Section: Introductionmentioning

confidence: 99%

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Bade

Bijlsma

Miller

et al. 2015

Science of The Total Environment

View full text Add to dashboard Cite

Abstract:The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (t R ) and mass spectral comparisons. Chromatographic t R prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for t R prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound t R dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85 % of all compounds to within 2 minutes of their measured t R for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95 % prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.

show abstract

Section: Introductionmentioning

confidence: 99%

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Bade

Bijlsma

Miller

et al. 2015

Science of The Total Environment

View full text Add to dashboard Cite

show abstract

“…The set of optimal parameter values were listed in Table 2 and the predicting results of test set on OV-1 and SE-54 were listed in Figures 1 and 2. It can be seen from the figures that the predictive values of gas chromatography retention index of oxygen-organic compounds applications of ANN have been adequately described elsewhere [7][8][9][10]. Extensive use of ANN, which has inherent ability to incorporate nonlinear and cross-product terms into the model and does not require prior knowledge of the mathematical function as well, largely rests on its flexibility and less sensitivity to collinearity among variables.…”

Section: Modelsmentioning

confidence: 99%

“…Commonly, they consist of three layers: one input layer formed by a number of neurons that equal to the number of descriptors, one out neuron (providing the model response) and a number of hidden neurons fully connected to both input and out neurons. Among the available learning algorithms, back-propagation of errors is one of the most widely used [8,15].…”

Section: Modelsmentioning

confidence: 99%

Study on Quantitative Structure-Retention Relationships (QSRR) for Oxygen-Containing Organic Compounds Based on Gene Expression Programming (GEP)

Zhang¹,

Shi²,

Ding³

et al. 2015

J Chromatogr Sep Tech

View full text Add to dashboard Cite

“…It is a typical practice nowadays to search for correlations between values of various measured physicochemical features of molecules or molecular systems and their predicted or measured structural or physicochemical characteristics [1][2][3][4][5][6][7]. Such correlations can be generally found using chemometric methods of analysis [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Chemometric outlook on correlations between retention parameters of polar and semipolar HPLC columns and physicochemical characteristics of ampholytic substances of biological and pharmaceutical relevance

et al. 2018

View full text Add to dashboard Cite

The Quantitative Property-Retention Relation (QPRR) approach was applied to analyze the correlations between the retention parameters of ampholytic, biologically active substances and their physicochemical (predicted/spectral) characteristics. The retention parameters were obtained for polar and semipolar HPLC columns at various compositions of mobile phases and pH conditions. These values are a unique collection of chromatographic parameters that are a measure of lipophilicity and, consequently, can be very helpful in assessing pharmacological potency of the compounds investigated. Three QPRR models that meet the predictive capability criteria were developed. The relationships can be used to gain pharmacologically interesting information on the biologically active ampholytic substances.

show abstract

Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds

Cited by 87 publications

References 51 publications

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Study on Quantitative Structure-Retention Relationships (QSRR) for Oxygen-Containing Organic Compounds Based on Gene Expression Programming (GEP)

Chemometric outlook on correlations between retention parameters of polar and semipolar HPLC columns and physicochemical characteristics of ampholytic substances of biological and pharmaceutical relevance

Contact Info

Product

Resources

About