ArticleACS omega2019
Large-Scale Target Identification of Herbal Medicine Using a Reverse Docking Approach.
Article in ACS omega, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed, 31 citations in OpenAlex.
- Harnessing AI-driven reverse docking in drug discovery: a comprehensive review of opportunities, challenges, and emerging trends.Journal of molecular modeling · 2025Review
- Identification and Analysis of Anticancer Therapeutic Targets from the Polysaccharide Krestin (PSK) and Polysaccharopeptide (PSP) Using Inverse Docking.Molecules (Basel, Switzerland) · 2024Article
- Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.Briefings in bioinformatics · 2022Article
- Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study.Bioinformatics and biology insights · 2022Article
- TAIGET: A small-molecule target identification and annotation web server.Frontiers in pharmacology · 2022Article
- How Physiologic Targets Can Be Distinguished from Drug-Binding Proteins.Molecular pharmacology · 2021Article
- LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds.Journal of cheminformatics · 2021Article
- Identification of the Molecular Mechanisms of Peimine in the Treatment of Cough Using Computational Target Fishing.Molecules (Basel, Switzerland) · 2020Article
- Casticin inhibits nasopharyngeal carcinoma growth by targeting phosphoinositide 3-kinase.Cancer cell international · 2019Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Herbal medicine has been used to countermine various diseases for centuries. However, most of the therapeutic targets underlying herbal therapy remain unclear, which largely slow down the novel drug discovery process from natural products. In this study, we developed a novel computational pipeline for assisting de novo identification of protein targets for herbal ingredients. The pipeline involves pharmacophore comparison and reverse ligand-protein docking simulation in a high throughput manner. We evaluated the pipeline using three traditional Chinese medicine ingredients such as acteoside, quercetin, and epigallocatechin gallate as examples. A majority of current known targets of these ingredients were successfully identified by the pipeline. Structural comparative analyses confirmed that the predicted ligand-target interactions used the same binding pockets and binding modes as those of known ligand-target interactions. Furthermore, we illustrated the mechanism of actions of the ingredients by constructing the pharmacological networks on the basis of the predicted target profiles. In summary, we proposed an efficient and economic option for large-scale target exploration in the herb study. This pipeline will be particularly valuable in aiding precise drug discovery and drug repurposing from natural products.
Identifiers
What OpenQuestion holds
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.