ArticleInternational journal of molecular sciences2020
Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial intelligence-based screening of phytochemicals for targeted cancer therapy.Natural products and bioprospecting · 2026Review
- Dihydrotanshinone as a Natural Product-Based CYP17A1 Lyase Inhibitor for Hyperandrogenic Disorders.Biomolecules · 2026Article
- Integrative network pharmacology and molecular dynamics analysis ofRSC advances · 2025Article
- Plant antibacterials: The challenges and opportunities.Heliyon · 2024Review
- Mitochondria and telomeres: hand in glove.Biogerontology · 2024Review
- Article
- The protective effects of alpha-pinene on high glucose-induced oxidative stress and inflammation in HepG2 cells.Iranian journal of basic medical sciences · 2024Article
- Synthesis of Pyrrolo[3,4-Pharmaceuticals (Basel, Switzerland) · 2023Article
- In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining.International journal of molecular sciences · 2022Article
- Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases.Marine drugs · 2022Article
- Identification of Potent Bioassay Guided Terpenoid and Glycoside Root Fractions ofInternational journal of microbiology · 2022Article
- Article
Corrections and comments
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Authors and funding
18 authors.
Funding
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 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, 17β-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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.