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| Item Type: | Article |
|---|---|
| Title: | Modeling Structure-Activity Relationship of AMPK Activation |
| Creators Name: | Drewe, J., Küsters, E., Hammann, F., Kreuter, M., Boss, P. and Schöning, V. |
| Abstract: | 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. |
| Keywords: | AMPK Activator, QSAR, Deep Learning, Logistic Regression, Machine Learning, Random Fores, Support Vector Machine |
| Source: | Molecules |
| ISSN: | 1420-3049 |
| Publisher: | Molecular Diversity Preservation International |
| Volume: | 26 |
| Number: | 21 |
| Page Range: | 6508 |
| Date: | 1 November 2021 |
| Official Publication: | https://doi.org/10.3390/molecules26216508 |
| PubMed: | View item in PubMed |
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