Hologram QSAR study on the critical micelle concentration of Gemini surfactants

Jiao, Long; Yuan, Wei; Qu, Le; Xue, Zhiwei; Ge, Yiqing; Liu, Huanhuan; Lei, Bin; Gao, Qian; Li, Mengke

doi:10.1016/j.colsurfa.2019.124226

Cited by 20 publications

(14 citation statements)

References 39 publications

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“…HQSAR is a new QSAR technology which is based on fragments and has high predictive ability. There is no need to determine the 3 D structure, molecular comparison and assumed conformation [21] , which has irreplaceable advantages in molecular design. All possible molecular fragments (such as linearity, branching, overlap, hybridization, and chirality) can be encoded in molecular holograms.…”

Section: Methodsmentioning

confidence: 99%

Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

Tong

Zhang

Luo

et al. 2021

Chinese Journal of Analytical Chemistry

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Section: Methodsmentioning

confidence: 99%

Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

Tong

Zhang

Luo

et al. 2021

Chinese Journal of Analytical Chemistry

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“…To analyze the influence of the dataset separation method on the overall quality of the QSAR models, seven datasets of different sizes and nature of dependent variable (biological activities, enzymatic inhibition, and physicochemical properties) were collected from the literature [32][33][34][35][36]. These sets of molecules were chosen by searching for datasets in which HQSAR models had previously been reported, as it is a commercial QSAR technique that is simple to implement and has good reproducibility.…”

Section: Datasetsmentioning

confidence: 99%

“…half maximal inhibitory concentration (IC50) converted to the pIC50 scale (-log IC50) against angiotensin converting enzyme (ACE), dihydrofolate reductase (DHFR), glycogen phosphorylase b (GPB) [32], mantle cell lymphoma (JEKO-1) [33], thyroid hormone receptors TRα and TRβ (TR) [34], cannabinoid receptor subtype CB1 (a member of G-protein-coupled receptor superfamily -GPCR) [35], and critical micelle concentration (CMC) converted to the pCMC scale (-log CMC) [36]. Except for activities against thyroid hormone receptors TRα and TRβ, which both subtypes corresponded to the same set of molecules, all activities corresponded to a single separate dataset.…”

Section: Datasetsmentioning

confidence: 99%

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MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling

Veríssimo

Panteleão

Gertrudes

et al. 2022

Preprint

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The use of computer-aided drug design has become an essential part of drug development. In this context, QSAR models capable of predicting biological activities, toxicity, and pharmacokinetic properties were widely used to search bioactive molecules in chemical databases for lead compounds. The preparation of dataset used to build these models has a strong influence on the quality of the generated models, and sampling these data requires that the original dataset be divided into training (used for model training) and test sets (used to further evaluate the predictability of the model). This sampling can be done randomly or rationally, but the rational or purposeful division is superior to the random method. In this paper, we present MASSA Algorithm ("Molecular dAta Set SAmpling Algorithm"), an open-source, user-friendly Python tool that can be used to perform automatic sampling of datasets of molecules into training and test sets by exploring the biological, physicochemical, and structural spaces of molecules using Principal Component Analysis, Hierarchical Clustering Analysis, and K-modes clustering. This proposed algorithm is very useful when the variables for QSAR model generation is not available or to construct multiple QSAR models with the same training and test sets. When compared to random sampling, the presented algorithm produces models with lower variability and higher values across multiple replicates for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, the tool also generates useful graphical representations of the distribution that can provide insights into the data.

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“…Gemini surfactants contains two hydrophobic tails and two hydrophilic heads connected by a spacer at or near the two head ( Figure 1) [2]. These chemical structure modifications grant them excellent properties namely, low CMC, high aqueous solubility and viscosity, unique micelle structure and aggregation behaviour and high yield in decreasing the surface tension of water solutions [3][4][5][6][7][8][9][10][11][12]. As the CMC is an important parameter to characterize surfactants, their measurement is usually difficult and costly.…”

Section: Introductionmentioning

confidence: 99%

CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

Laidi

Abdallah

Si‐Moussa

et al. 2021

CI&CEQ

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As quantitative structure-property relationship (QSPR) technique provides a suitable tool to predict the critical micelle concentration (CMC) of Gemini surfactants from their structure descriptors. In this study, a comparative work was conducted to model the CMC property of 211 diverse Gemini surfactants based on their structural characteristics using linear and non-linear quantitative structure-property relationship models. Least squares model (OLS) and partial least squares (PLS) against k-nearest neighbours regression model (KNN), artificial neural network (ANN) and support vector regression (SVR) have been developed to model the CMC. Molecular descriptors were calculated and screened to remove unsuitable descriptors and improve the learning. Results indicate that the improved performance of support vector regression when the hyper-parameters are optimized using Dragonfly algorithm (SVR-DA) was highly capable of predicting the pCMC (-logCMC) values with an average absolute relative deviation (AARD) of 0.666 and coefficient of determination (R?) of 0.9971 for the global dataset.

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Hologram QSAR study on the critical micelle concentration of Gemini surfactants

Cited by 20 publications

References 39 publications

Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling

CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

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