Prediction of gas chromatographic retention indexes for diverse drug compounds

Rohrbaugh, R. H.; Jurs, Peter C.

doi:10.1021/ac00171a018

Cited by 32 publications

(8 citation statements)

References 17 publications

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“…Chromatographic retention is based on the interactions between the solute and the stationary phase, and the goal of the present work is to find which of the available topological, geometrical, electronic and physical descriptors that we computed are related to the retention of the anabolic steroids. In the past, this goal has been approached successfully for many compound classes and chromatographic systems (3)(4)(5)(6)(7)(8)(9). Multiple linear regression analysis was used to find the subset of descriptors that correlates the RRTs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of gas chromatographic relative retention times of anabolic steroids

Og²,

Jc³

et al. 1991

Anal. Chem.

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The prediction of gas chromatographic relative retention times (RRTs) of anabolic steroids, used in the doping control of athletes, was performed by a quantitative structure-retention relationship (QSRR) and multiple linear regression analysis study. A nine-variable model was generated with a multiple correlation coefficient R = 0.991 and relative standard error of less than 3%. Preliminary results indicated that the application of the model, especially in the prediction of RRTs of metabolites of the anabolic steroids, will be helpful.

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Section: Introductionmentioning

confidence: 99%

Prediction of gas chromatographic relative retention times of anabolic steroids

Og²,

Jc³

et al. 1991

Anal. Chem.

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“…For molecular polarizabilities, empirical dipole-based models have long been available in the literature 21,22 and used to predict, e.g., retention times (RT) in chromatographic applications. 23 We have developed the open-source computational framework kallisto, 24 which enables the efficient calculation of multiple physicochemical descriptors like atomic partial charges or polarizabilities. In the next section, we introduce the theoretical foundations for calculating such atomic and molecular features.…”

Section: Introductionmentioning

confidence: 99%

An open-source framework for fast-yet-accurate calculation of quantum mechanical features

Caldeweyher

Bauer²,

Tehrani³

2021

Preprint

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We present the open-source framework kallisto that enables the efficient and robust calculation of quantum mechanical features for atoms and molecules. For a benchmark set of 49 experimental molecular polarizabilities, the predictive power of the presented method competes against second-order perturbation theory in a converged atomic-orbital basis set at a fraction of its computational costs. Robustness tests within a diverse validation set of more than 80,000 molecules show that the calculation of isotropic molecular polarizabilities has a low failure-rate of only 0.3 %. We present furthermore a generally applicable van der Waals radius model that is rooted on atomic static polarizabilites. Efficiency tests show that such radii can even be calculated for small- to medium-size proteins where the largest system (SARS-CoV-2 spike protein) has 42,539 atoms. Following the work of Domingo-Alemenara et al. [Domingo-Alemenara et al., Nat. Comm., 2019, 10, 5811], we present computational predictions for retention times for different chromatographic methods and describe how physicochemical features improve the predictive power of machine-learning models that otherwise only rely on two-dimensional features like molecular fingerprints. Additionally, we developed an internal benchmark set of experimental super-critical fluid chromatography retention times. For those methods, improvements of up to 17 % are obtained when combining molecular fingerprints with physicochemical descriptors. Shapley additive explanation values show furthermore that the physical nature of the applied features can be retained within the final machine-learning models. We generally recommend the kallisto framework as a robust, low-cost, and physically motivated featurizer for upcoming state-of-the-art machine-learning studies.

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“…A branch of QSAR, QSRR (quantitative structure-retention relationship), is based on relating structures with their chromatographic retention parameters (2). This goal has been approached successfully in the past for many chromatographic systems and compound classes (3)(4)(5)(6)(7)(8)(9). From those studies, some inferences can be extracted in relation to what conditions must be fulfilled by the data set so that in building a chromatographic statistical model, it would have errors of the same magnitude as the experimental error.…”

Section: Introductionmentioning

confidence: 99%

Prediction of gas chromatographic relative retention times of stimulants and narcotics

Georgakopoulos¹,

Kiburis²,

Jurs³

1991

Anal. Chem.

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The ADAPT software system was used to create models for the prediction of gas chromatographic relative retention times (RRTs) of stimulants and narcotics that are analyzed in doping control of athletes. The two main methods that were followed for building the models were the quantitative structure-retention relationship (QSRR) and multiple linear regression analysis. The main proposed model for the entire data set had a multiple correlation coefficient R = 0.991 and standard error s = 0.046 or approximately 4.5%. Because of the relatively high standard error of the main model, a second model was built on a subset of compounds with R = 0.982 and s = 0.027 or approximately 2.5%.

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Prediction of gas chromatographic retention indexes for diverse drug compounds

Cited by 32 publications

References 17 publications

Prediction of gas chromatographic relative retention times of anabolic steroids

Prediction of gas chromatographic relative retention times of anabolic steroids

An open-source framework for fast-yet-accurate calculation of quantum mechanical features

Prediction of gas chromatographic relative retention times of stimulants and narcotics

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