ArticleMolecular diversity2014
An in silico protocol for identifying mTOR inhibitors from natural products.
Article in Molecular diversity, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Exploring the Potential of Dolutegravir in Alzheimer's Disease Treatment: Insights from Network Pharmacology andCentral nervous system agents in medicinal chemistry · 2026Article
- Molecular docking analysis of mTOR protein kinase with chromatographically characterized compounds fromBioinformation · 2022Article
- Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells.Frontiers in pharmacology · 2021Article
- Discovering new PI3Kα inhibitors with a strategy of combining ligand-based and structure-based virtual screening.Journal of computer-aided molecular design · 2018Article
- Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis.BioMed research international · 2017Article
- Structure-activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches.OncoTargets and therapy · 2016Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mammalian target of rapamycin (mTOR) is an anti-cancer target. In this study, we propose an in silico protocol for identifying mTOR inhibitors from the ZINC natural product database. First, a three-dimensional quantitative structure-activity relationship pharmacophore model was built based on known mTOR inhibitors. The model was validated with an external test set, Fischer's randomization method, a decoy set and pharmacophore mapping conformation testing. The results showed that the model can predict the mTOR inhibition activity of the tested compounds. Virtual screening was performed based on the best pharmacophore model, and the results were then filtered using a molecular docking approach. In addition, molecular mechanics/generalized born surface area analysis was used to refine the selected candidates. The top 20 natural products were selected as potential mTOR inhibitors, and their structural scaffolds could serve as building blocks in designing drug-like molecules for mTOR inhibition.
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