Modeling Structure-Activity Relationship of AMPK Activation.

Drewe, Jürgen; Küsters, Ernst; Hammann, Felix; Kreuter, Matthias; Boss, Philipp; Schöning, Verena (2021). Modeling Structure-Activity Relationship of AMPK Activation. Molecules, 26(21) Molecular Diversity Preservation International MDPI 10.3390/molecules26216508

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of General Internal Medicine (DAIM) > Clinic of General Internal Medicine

UniBE Contributor:

Hammann, Felix and Schöning, Verena

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1420-3049

Publisher:

Molecular Diversity Preservation International MDPI

Language:

English

Submitter:

Tobias Tritschler

Date Deposited:

16 Dec 2021 08:31

Last Modified:

16 Dec 2021 08:31

Publisher DOI:

10.3390/molecules26216508

PubMed ID:

34770917

Uncontrolled Keywords:

AMPK activator QSAR deep learning logistic regression machine learning random forest support vector machine

BORIS DOI:

10.48350/162450

URI:

https://boris.unibe.ch/id/eprint/162450

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