Molecular modeling studies of benzothiophene-containing derivatives as promising selective estrogen receptor downregulators: a combination of 3D-QSAR, molecular docking and molecular dynamics simulations

Zhang, Cuihua; Li, Qunlin; Ren, Yujie; Liu, Fei

doi:10.1080/07391102.2020.1751717

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…Q 2 , with a value of 0.904, represents the strong predictive ability of the model. Typically, R 2 and Q 2 values exceeding 0.5 indicate a high level of tting accuracy (Zhang et al, 2021). To assess the robustness of the PLS-DA model, we performed 200 random permutations of the variables in the de ned classi cation Y matrix and present the results in Fig.…”

Section: Construction Of Pls-da Modelmentioning

confidence: 99%

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Ji,

Ding

2023

Food Anal. Methods

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Traditional fermented shrimp paste is wildly recognized for its distinctive avor, exceptional nutritional value, and numerous health bene ts. The volatile components of shrimp paste can be in uenced by the production process and geographical environment, thereby re ecting its unique characteristics and quality. The objective of this study was to investigate the correlation between shrimp paste produced from different geographical origins and establish a rapid and accurate method for distinguishing them. To accomplish these objectives, the volatiles of shrimp paste from various regions were extracted using headspace solid-phase micro-extraction (HS-SPME) and subsequently analyzed by GC-MS. The data is preprocessed through MS-DIAL and then subjected to multivariate statistical analysis, wherein correlation analysis, principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and orthogonal partial least square-discriminant analysis (OPLS-DA) were collectively employed. The ndings demonstrate that OPLS-DA exhibits a favorable discriminant region in the context of discriminant analysis. The integration of untargeted GC-MS metabolomics with multivariate statistical analysis offers a rapid and e cient approach for discriminating shrimp paste originating from diverse regions, which circumvents the time-intensive process of volatile compound identi cation.

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Section: Construction Of Pls-da Modelmentioning

confidence: 99%

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Ji,

Ding

2023

Food Anal. Methods

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show abstract

“…Since the affinity of chemical substances to protein enzymes and receptors strongly depends on intermolecular interactions, such as hydrogen bonding, electrostatic interactions, and hydrophobic interactions, it has been observed that the complementary positional relationship between the base groups of the chemicals and their binding site of the enzyme or receptor required for the interactions of chemical compounds is important (Yang et al, 2017;Kimani et al, 2018;Pan et al, 2019;Garcia et al, 2020). Therefore, besides molecular descriptors that show physiochemical properties related to intermolecular interactions, QSAR analysis in a 3D space that utilizes descriptors representing the 3D structure of chemical substances has been recently attracting research attention (Chen et al, 2020;Hadni and Elhallaoui, 2020;Kumar et al, 2020;Liu et al, 2020;Zhang et al, 2020). Additionally, prediction models in classical QSAR are constructed using descriptors as explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Matsuzaka¹,

Uesawa²

2022

Current Issues in Molecular Biology

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The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as in silico computational assessment for a long time. Further, owing to the high-performance modeling of QSAR, machine learning methods have been developed and upgraded. Particularly, the threedimensional structure of chemical compounds has been gaining increasing attention owing to the representation of a large amount of information. However, only many of feature extraction is impossible to build models with the high-ability of the prediction. Thus, suitable extraction and effective selection of features are essential for models with excellent performance. Recently, the deep learning method has been employed to construct prediction models with very high performance using big data, especially, in the field of classification. Therefore, in this study, we developed a molecular image-based novel QSAR approach, called DeepSnap-Deep learning approach for designing high-performance models. In addition, this DeepSnap-Deep learning approach outperformed the conventional machine learnings when they are compared. Herein, we discuss the advantage and disadvantages of the machine learnings as well as the availability of the DeepSnap-Deep learning approach.

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Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Ji,

Ding

2023

Preprint

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Traditional fermented shrimp paste is wildly recognized for its distinctive flavor, exceptional nutritional value, and numerous health benefits. The volatile components of shrimp paste can be influenced by the production process and geographical environment, thereby reflecting its unique characteristics and quality. The objective of this study was to investigate the correlation between shrimp paste produced from different geographical origins and establish a rapid and accurate method for distinguishing them. To accomplish these objectives, the volatiles of shrimp paste from various regions were extracted using headspace solid-phase micro-extraction (HS-SPME) and subsequently analyzed by GC-MS. The data is preprocessed through MS-DIAL and then subjected to multivariate statistical analysis, wherein correlation analysis, principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and orthogonal partial least square-discriminant analysis (OPLS-DA) were collectively employed. The findings demonstrate that OPLS-DA exhibits a favorable discriminant region in the context of discriminant analysis. The integration of untargeted GC-MS metabolomics with multivariate statistical analysis offers a rapid and efficient approach for discriminating shrimp paste originating from diverse regions, which circumvents the time-intensive process of volatile compound identification.

show abstract

Molecular modeling studies of benzothiophene-containing derivatives as promising selective estrogen receptor downregulators: a combination of 3D-QSAR, molecular docking and molecular dynamics simulations

Cited by 5 publications

References 49 publications

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

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