Prediction of polar surface area of drug molecules: A QSPR approach

Noorizadeh, Hadi; Farmany, Abbas; Noorizadeh, Mehrab; Kohzadi, M.

doi:10.1002/dta.288

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…However, the negative t ‐value of tPSA could not be interpreted with the HILIC theory. Since tPSA and logD represent the hydrophilicity and hydrophobicity of the compound respectively [32], their correlations with RT should be reversed. However, the similar correlations of logD and tPSA with RT were unexpectedly obtained.…”

Section: Resultsmentioning

confidence: 99%

Study on the separation mechanism of solid‐substrate electrospray ionization mass spectrometry

Shi

et al. 2021

J of Separation Science

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Solid‐substrate electrospray ionization mass spectrometry is an important ambient ionization technology to simplify mass spectrometry analysis. Nowadays, its separation application has been reported increasingly, however, the detailed separation mechanism is still indistinct although the chromatographic effect was reported as a possible factor. In this study, substrate‐filled capillary electrospray ionization mass spectrometry was developed as an ideal model to investigate the separation mechanism using over thirty small molecules (neutral, basic, and weakly acidic) as model compounds with C18‐bonded silica gel and silica gel as the substrates. The chromatographic effect was validated by the negative t‐value of oil‐water distribution coefficient, and the electric field effect was verified by the paired t‐test (p < 0.01) between the retention times at 5.5 and 4.0 kV. A differential equation was proposed to quantify the compound retention under electric field. The quantitative method was validated to rapidly quantify proline (31.88 μg/mL) and hydroxyproline (20.71 μg/mL) in plasma with acceptable selectivity and accuracy. In conclusion, the separation mechanism of solid‐substrate electrospray ionization mass spectrometry was the combination of the chromatographic and electric field effects, which could provide theoretical guidance for the separation optimization, and also promote its applications in biological, pharmaceutical, forensic, food and environmental analyses, etc.

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Section: Resultsmentioning

confidence: 99%

Study on the separation mechanism of solid‐substrate electrospray ionization mass spectrometry

Shi

et al. 2021

J of Separation Science

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“…Similarly, more recent studies based on QSPR ANN model predicted octanol-water partition coefficients for 209 chlorinated trans-azobenzene derivatives, contaminants in herbicides [83]. QSPR ANN model was also used by Noorizadeh et al to calculate the polar surface area of 32 drug molecules [84]. Application of ANNs in prediction of API solubility has been demonstrated by Louis and colleagues in comparison study of MLR, ANN, and SVM methods with ANN yielding the best prediction data set [85].…”

Section: Quantitative Structure-activity Relationships (Qsar) and Quamentioning

confidence: 97%

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Sutariya¹,

Groshev²,

Sadana³

et al. 2013

TOBIOIJ

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Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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Synthesis, evaluation of anticancer activity and COMPARE analysis of N-bis(trifluoromethyl)alkyl-N’-substituted ureas with pharmacophoric moieties

Luzina

Popov

2012

European Journal of Medicinal Chemistry

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Prediction of polar surface area of drug molecules: A QSPR approach

Cited by 5 publications

References 44 publications

Study on the separation mechanism of solid‐substrate electrospray ionization mass spectrometry

Study on the separation mechanism of solid‐substrate electrospray ionization mass spectrometry

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Synthesis, evaluation of anticancer activity and COMPARE analysis of N-bis(trifluoromethyl)alkyl-N’-substituted ureas with pharmacophoric moieties

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