In order to make programmed-temperature retention index (PTRI) data be shared by other chromatographers and laboratories, conversion of PTRI from one set of experimental conditions to another is investigated in detail in this work. It was found that the differences between the PTRIs at different heating rates are structurally dependent, especially the number of ring in molecules. Thus, with the help of molecule constitutional descriptors, equations of PTRI conversion to certain initial temperature, heating rate, and stationary phase were obtained with high correlation coefficients and low standard deviations. Calculation errors of PTRI conversion between different heating rates and between different initial temperatures were from 1.1 to 2.9 retention index units (i.u.), which is in the same order with experiment errors. It is well known that reproducibility of PTRI on a polar column is not as good as that on an apolar column because of the apolarity of the n-alkane homologues. Thus, topological descriptors were used for PTRI conversion between two columns with different polar stationary phases, giving better results than those obtained by constitutional descriptors. This shows that topological descriptors could provide more molecular structural information than constitutional descriptors. However, as constitutional descriptor has the advantages of clear physical meaning and very simple calculation, it is our first selection when the PTRI calculation accuracy is satisfied. The method developed is simple in calculation, easy to be performed with high accuracy.
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http://dx.doi.org/10.1016/j.chroma.2007.01.040 | DOI Listing |
J Chem Inf Model
January 2025
Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China.
Drug-induced liver injury (DILI) is a major challenge in drug development, often leading to clinical trial failures and market withdrawals due to liver toxicity. This study presents StackDILI, a computational framework designed to accelerate toxicity assessment by predicting DILI risk. StackDILI integrates multiple molecular descriptors to extract structural and physicochemical features, including the constitution, pharmacophore, MACCS, and E-state descriptors.
View Article and Find Full Text PDFJ Sep Sci
November 2024
A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Moscow, Russia.
Retention index prediction based on the molecule structure is not often used in practice due to low accuracy, the need to use paid software to calculate molecular descriptors (MD), and the narrow applicability domain of many models. In recent years, relatively accurate and versatile deep learning (DL)-based models have emerged. These models are now used in practice as an additional criterion in gas chromatography-mass spectrometry identification.
View Article and Find Full Text PDFAnal Chem
August 2024
Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, 500 05 Hradec Králové, Czechia.
The retention behavior in supercritical fluid chromatography and its stability over time are still unsatisfactorily explained phenomena despite many important contributions in recent years, especially focusing on linear solvation energy relationship modeling. We studied polar stationary phases with predominant -OH functionalities, i.e.
View Article and Find Full Text PDFJ Chem Theory Comput
June 2024
Department of Chemistry, New York University, New York, New York 10003, United States.
Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation.
View Article and Find Full Text PDFJ Chem Inf Model
June 2024
Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States.
The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations such as large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool.
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