Identification of potential AMPK activator by pharmacophore modeling, molecular docking and QSAR study

Li, Yingying; Peng, Jiale; Li, Penghua; Du, Haibo; Li, Yaping; Liu, Xingyong; Zhang, Li; Wang, Liangliang; Zuo, Zhili

doi:10.1016/j.compbiolchem.2019.02.007

Cited by 10 publications

(10 citation statements)

References 33 publications

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“…In the literature (PubMed search with terms "AMPK" and "QSAR"), several QSAR models for prediction of AMPK activation have been published so far [24][25][26][27][28][29][30]. However, all of these models used pharmacophore docking analyses or homology models or structure-, ligand-, or fragment-based design and focused exclusively on direct AMPK activators.…”

Section: Discussionmentioning

confidence: 99%

Modeling Structure–Activity Relationship of AMPK Activation

Drewe

Küsters²,

Hammann

et al. 2021

Molecules

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The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and also cancer—activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.

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Section: Discussionmentioning

confidence: 99%

Modeling Structure–Activity Relationship of AMPK Activation

Drewe

Küsters²,

Hammann

et al. 2021

Molecules

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“…For the screening database, the specs database(http://www.specs.net) containing 212,736 compounds were rst ltered by Lipinski and Veber rules and 75671 compounds obeyed the Lipinski's cut-off values [20]. Subsequently, the remaining molecules were prepared with LigPrep module provided in the Maestro 12.1 (www.schrodinger.com) using the OPLS_2005 force led and the protonation states were generated with Epik at PH of 7.4.…”

Section: Data Preparationmentioning

confidence: 99%

Discovery of Novel Inhibitors Targeting HDM2: Virtual Screening, Molecular Dynamics Simulation, Binding affinity predication and Cell Assay

Feng

Liang

Xiong

et al. 2022

Preprint

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HDM2(human double-minute 2 protein) is an important drug target for tumor treatment. The main physiological function of HDM2 is to negatively regulate the tumor suppressor protein p53 and inhibit the normal biological function of p53, which leads to the occurrence and development of tumors. Therefore, the discovery of small molecules that can inhibit the activity of HDM2 is a promising cancer therapy. In this study, we reported a virtual screening as an effective strategy to discover potential HDM2 inhibitors from the Specs database. Then 100 ns all-atom molecular dynamics (MD) simulation and binding free energies calculated were carried out on the selected compounds, and cell assay showed that Hit 3 was the most active against SMMC-7721 cell line with an IC50 of 14.14μM. In conclusion, we reported seven prospective candidates with novel skeletons among them Hit 3 is a potential HDM2 inhibitor and provided an insight into the mechanism of interaction of the compounds to HDM2 target.

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“…QSAR models can be defined as regression or classification models by using different computational strategies [3]. The features are related with biological activities by using statistical methods or artificial intelligence approaches, such as Multiple Linear Regression (MLR) [4], Support Vector Regression (SVR) [5], Boosted Tree [6], and Partial Least Squares (PLS) regression [7], etc. In particular, machine learning methods have become extensively used in this field during the last few years [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Generate a new food source according to Equation (5) and convert it into discrete values by using Equation (7). 17:…”

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confidence: 99%

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An Improved Artificial Bee Colony for Feature Selection in QSAR

Lin

Wang

et al. 2021

Algorithms

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Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy and interpretability of QSAR by removing redundant or irrelevant molecular descriptors. An artificial bee colony algorithm (ABC) that mimics the foraging behaviors of honey bee colony was originally proposed for continuous optimization problems. It has been applied to feature selection for classification but seldom for regression analysis and prediction. In this paper, a binary ABC algorithm is used to select features (molecular descriptors) in QSAR. Furthermore, we propose an improved ABC-based algorithm for feature selection in QSAR, namely ABC-PLS-1. Crossover and mutation operators are introduced to employed bee and onlooker bee phase to modify several dimensions of each solution, which not only saves the process of converting continuous values into discrete values, but also reduces the computational resources. In addition, a novel greedy selection strategy which selects the feature subsets with higher accuracy and fewer features helps the algorithm to converge fast. Three QSAR datasets are used for the evaluation of the proposed algorithm. Experimental results show that ABC-PLS-1 outperforms PSO-PLS, WS-PSO-PLS, and BFDE-PLS in accuracy, root mean square error, and the number of selected features. Moreover, we also study whether to implement scout bee phase when tracking regression problems and drawing such an interesting conclusion that the scout bee phase is redundant when dealing with the feature selection in low-dimensional and medium-dimensional regression problems.

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Identification of potential AMPK activator by pharmacophore modeling, molecular docking and QSAR study

Cited by 10 publications

References 33 publications

Modeling Structure–Activity Relationship of AMPK Activation

Modeling Structure–Activity Relationship of AMPK Activation

Discovery of Novel Inhibitors Targeting HDM2: Virtual Screening, Molecular Dynamics Simulation, Binding affinity predication and Cell Assay

An Improved Artificial Bee Colony for Feature Selection in QSAR

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