Identification of hit compounds for squalene synthase: Three‐dimensional quantitative structure‐activity relationship pharmacophore modeling, virtual screening, molecular docking, binding free energy calculation, and molecular dynamic simulation

Hou, Zhengmeng; Yan, Guohua; Ma, Xiaojie; Luo, Jianlin; Hou, Xueyan; Pu, Chun; Han, Xiaolei; Zhang, W.; Zhang, M.; Shi, Jianyou; Li, R.

doi:10.1002/cem.2923

Cited by 5 publications

(4 citation statements)

References 24 publications

(25 reference statements)

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“…According to the literature, researchers have constructed pharmacophore models of SQS [ 15 , 16 ]. We further compared our pharmacophore model to those of these researchers.…”

Section: Resultsmentioning

confidence: 99%

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Chen

Luo

et al. 2018

Molecules

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Squalene synthase (SQS), a key downstream enzyme involved in the cholesterol biosynthetic pathway, plays an important role in treating hyperlipidemia. Compared to statins, SQS inhibitors have shown a very significant lipid-lowering effect and do not cause myotoxicity. Thus, the paper aims to discover potential SQS inhibitors from Traditional Chinese Medicine (TCM) by the combination of molecular modeling methods and biological assays. In this study, cynarin was selected as a potential SQS inhibitor candidate compound based on its pharmacophoric properties, molecular docking studies and molecular dynamics (MD) simulations. Cynarin could form hydrophobic interactions with PHE54, LEU211, LEU183 and PRO292, which are regarded as important interactions for the SQS inhibitors. In addition, the lipid-lowering effect of cynarin was tested in sodium oleate-induced HepG2 cells by decreasing the lipidemic parameter triglyceride (TG) level by 22.50%. Finally. cynarin was reversely screened against other anti-hyperlipidemia targets which existed in HepG2 cells and cynarin was unable to map with the pharmacophore of these targets, which indicated that the lipid-lowering effects of cynarin might be due to the inhibition of SQS. This study discovered cynarin is a potential SQS inhibitor from TCM, which could be further clinically explored for the treatment of hyperlipidemia.

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“…According to the literature, researchers have constructed pharmacophore models of SQS [ 15 , 16 ]. We further compared our pharmacophore model to those of these researchers.…”

Section: Resultsmentioning

confidence: 99%

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Chen

Luo

et al. 2018

Molecules

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“…Quantitative structure-activity relationship (QSAR) methods using machine learning has been applied in various fields, such as chemistry, pharmacology, biology, materials, and environmental sciences [10][11][12][13][14] . However, many traditional QSAR models are based on small data sets including hundreds or even tens of molecules.…”

Section: Introductionmentioning

confidence: 99%

Deep electron cloud-activity and field-activity relationships

Yang

2023

Preprint

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Chemists have been pursuing the general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure-activity relationship (QSAR) models have limited application domains, e.g., they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This paper attempts to develop a new QSAR method that could theoretically predict various properties of molecules with diverse structures. The proposed deep electron cloud-activity relationships (DECAR) and deep field-activity relationships (DFAR) methods consist of three essentials: (1) A large number of molecule entities with activity data as training objects and responses; (2) three-dimensional electron cloud density (ECD) or related field data by the accurate density functional theory methods as input descriptors; (3) a deep learning model that is sufficiently flexible and powerful to learn the large data described above. DECAR and DFAR are used to distinguish 977 sweet and 1965 non-sweet molecules (with 6-fold data augmentation) and the classification performance is demonstrated to be significantly better than the traditional least squares support vector machine (LS-SVM) models using traditional descriptors. DECAR and DFAR would provide a feasible and promising way to establish a widely applicable, cumulative, and shareable artificial intelligence-driven QSAR system. They will promote the development of an interactive platform to collect and share the accurate ECD and field data of millions of molecules with annotated activities. With enough input data, we envision the appearance of hundreds of deep networks trained for various molecular activities. Finally, we could anticipate a single DECAR or DFAR network to learn and infer various properties of interest for chemical molecules, which will become an open and shared learning and inference tool for chemists.

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“…8,9 Quantitative structure-activity relationship (QSAR) methods using machine learning have been applied in various fields, such as chemistry, pharmacology, biology, materials, and environmental sciences. [10][11][12][13][14] However, many traditional QSAR models are based on small data sets, including hundreds or even tens of molecules. Although they can sometimes obtain good predictions and interpretability, their application range is limited, and they usually perform poorly in predicting the properties of molecules with diverse parent structures.…”

Section: Introductionmentioning

confidence: 99%

“…Quantitative structure‐activity relationship (QSAR) methods using machine learning have been applied in various fields, such as chemistry, pharmacology, biology, materials, and environmental sciences 10–14 . However, many traditional QSAR models are based on small data sets, including hundreds or even tens of molecules.…”

Section: Introductionmentioning

confidence: 99%

Deep electron cloud‐activity and field‐activity relationships

Yang

2023

Journal of Chemometrics

View full text Add to dashboard Cite

Chemists have been pursuing general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure‐activity relationship (QSAR) models have limited application domains; for example, they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This paper attempts to develop a new QSAR method that is theoretically possible to predict various properties of molecules with diverse structures. The proposed deep electron cloud‐activity relationships (DECAR) and deep field‐activity relationships (DFAR) methods consist of three essentials: (1) a large number of molecule entities with activity data as training objects and responses; (2) three‐dimensional electron cloud density (ECD) or related field data by the accurate density functional theory methods as input descriptors; and (3) a deep learning model that is sufficiently flexible and powerful to learn the large data described above. DECAR and DFAR are used to distinguish 977 sweet and 1965 non‐sweet molecules (with 6‐fold data augmentation), and the classification performance is demonstrated to be significantly better than the traditional least squares support vector machine (LS‐SVM) models using traditional descriptors. DECAR and DFAR would provide a possible way to establish a widely applicable, cumulative, and shareable artificial intelligence‐driven QSAR system. They are likely to promote the development of an interactive platform to collect and share the accurate ECD and field data of millions of molecules with annotated activities. With enough input data, we envision the appearance of several deep networks trained for various molecular activities. Finally, we could anticipate a single DECAR or DFAR network to learn and infer various properties of interest for chemical molecules, which will become an open and shared learning and inference tool for chemists.

show abstract

Identification of hit compounds for squalene synthase: Three‐dimensional quantitative structure‐activity relationship pharmacophore modeling, virtual screening, molecular docking, binding free energy calculation, and molecular dynamic simulation

Cited by 5 publications

References 24 publications

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Deep electron cloud-activity and field-activity relationships

Deep electron cloud‐activity and field‐activity relationships

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