Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction
Abstract
ABSTRACT 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 large scale target prediction of natural products are still rare. We have 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, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target prediction and guidance on using the respective tools.
How strong is the evidence?
Evidence Hub reads this as a preprint, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Strategy for Mitigating DKA Risk in Patients with Type 1 Diabetes on Adjunctive Treatment with SGLT Inhibitors: A STICH Protocol
- Glucoraphanin: a broccoli sprout extract that ameliorates obesity-induced inflammation and insulin resistance
- SGLT2 Inhibitors in Heart Failure: Targeted Metabolomics and Energetic Metabolism
- Mechanisms of blood pressure reduction with sodium-glucose co-transporter 2 (SGLT2) inhibitors
- Sodium-glucose cotransporter inhibitors: beyond glycaemic control
- Decoding the effects of SGLT2 inhibitors on cardiac arrhythmias in heart failure
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.