A quantitative prediction of the viscosity of ionic liquids using Sσ-profilemolecular descriptors

2015 ◽  
Vol 17 (5) ◽  
pp. 3761-3767 ◽  
Author(s):  
Yongsheng Zhao ◽  
Ying Huang ◽  
Xiangping Zhang ◽  
Suojiang Zhang
Keyword(s):  
Ionic Liquids ◽  
Molecular Descriptors ◽  
Quantitative Prediction ◽  
Qspr Study

A QSPR study of ILs using MLR and SVM algorithms based on COSMO-RS molecular descriptors (Sσ-profile).

Application of Ionic Liquids for the Determination of Lipophilicity Parameters Using TLC Method, and QSRR Analysis for the Antipsychotic Drugs

2020 ◽  
Vol 16 (7) ◽  
pp. 848-859
Author(s):  
Dominik Mieszkowski ◽  
Marcin Koba ◽  
Michał P. Marszałł
Keyword(s):  
Ionic Liquids ◽  
Antipsychotic Drugs ◽  
Molecular Descriptors ◽  
Harmful Effect ◽  
Correlation Coefficients ◽  
Reversed Phase ◽  
Assessment Process ◽  
Phase System ◽  
Phase Liquid ◽  
Lipophilicity Parameters

Background: Reversed-phase liquid chromatography may cause difficulties, especially in the case of basic drugs due to the strong silanophilic interactions in the partition mechanism. Recently, imidazolium-based ionic liquids additives appeared interesting and a convenient solution for suppressing the harmful effect of free residuals of silanol groups, allowing remodeling of the stationary/mobile-phase system, and thus improving the lipophilicity assessment process. Objective: The aim of the study was to evaluate the retention behavior of basic antipsychotics using various RP-LC systems, and compare them with data obtained from the modified ionic-liquids RP-TLC systems, and perform the QSRR analysis. Methods: Retention and lipophilicity parameters of diverse antipsychotics have been examined in various RP-LC systems. Lipophilicity indices were compared with miscellaneous computed logP values. Furthermore, a large number of molecular descriptors have been computed and compared using various medicinal chemistry software, in order to contribute to the analysis of QSRR. Results: Designated correlation coefficients showed that lipophilicity parameters from TLC systems without [EMIM][BF4] additive correlates very poor with the calculated logPs indices, whereas the indices from the traditional HPLC and TLC systems (with [EMIM][BF4]) were clearly better. Furthermore, QSRR analysis performed for these experimentally obtained lipophilicity parameters showed significant relationships between the retention constants (RO>M, logkw) and the in silico calculated physicochemical molecular descriptors. Conclusion: ILs additive may be a significant factor affecting the lipophilicity of basic compounds, thus their use may be favorable in lipophilicity assessment studies. QSRR models with ILs showed that they may be useful in searching/or predicting HPLC/TLC retention parameters for the new/other antipsychotic drugs.

Prediction of cellulose dissolution in ionic liquids using molecular descriptors based QSAR model

2016 ◽  
Vol 215 ◽  
pp. 541-548 ◽  
Author(s):  
Ngoc Lan Mai ◽  
Chan Kyung Kim ◽  
Byungho Park ◽  
Heon-Jin Park ◽  
Sang Huyn Lee ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Cellulose Dissolution

scholarly journals Quantitative prediction model for affinity of drug–target interactions based on molecular vibrations and overall system of ligand-receptor

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xian-rui Wang ◽  
Ting-ting Cao ◽  
Cong Min Jia ◽  
Xue-mei Tian ◽  
Yun Wang
Keyword(s):  
Drug Target ◽  
Prediction Models ◽  
Molecular Descriptors ◽  
Quantitative Structure Activity Relationship ◽  
High Accuracy ◽  
Quantitative Prediction ◽  
Maximal Effect ◽  
Molecular Vibrations ◽  
Wide Applicability ◽  
Drug Molecules

Abstract Background The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs. Results After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R2) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs. Conclusion Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors

2015 ◽  
Vol 26 (6) ◽  
pp. 499-506 ◽  
Author(s):  
S.E. Fioressi ◽  
D.E. Bacelo ◽  
W.P. Cui ◽  
L.M. Saavedra ◽  
P.R. Duchowicz
Keyword(s):  
Molecular Descriptors ◽  
Polymer Chemistry ◽  
Refractive Indices ◽  
Qspr Study
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