Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 21, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/ijms21197102
Keywords
in silico target prediction; dihydrochalcones; SEA; SwissTargetPrediction; SuperPred; polypharmacology; virtual screening
Funding
- GECT Euregio Tirol-Sudtirol-Trentino [IPN55]
- FWF Hertha Firnberg fellowship [T-942]
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Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17 beta-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.
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