Literature DB >> 28699534

Discovery and Development of ATP-Competitive mTOR Inhibitors Using Computational Approaches.

Yao Luo1, Ling Wang1.   

Abstract

The mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. This protein is an attractive target for new anticancer drug development. Significant progress has been made in hit discovery, lead optimization, drug candidate development and determination of the three-dimensional (3D) structure of mTOR. Computational methods have been applied to accelerate the discovery and development of mTOR inhibitors helping to model the structure of mTOR, screen compound databases, uncover structure-activity relationship (SAR) and optimize the hits, mine the privileged fragments and design focused libraries. Besides, computational approaches were also applied to study protein-ligand interactions mechanisms and in natural product-driven drug discovery. Herein, we survey the most recent progress on the application of computational approaches to advance the discovery and development of compounds targeting mTOR. Future directions in the discovery of new mTOR inhibitors using computational methods are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Entities:  

Keywords:  Molecular docking; drug discovery; homology modeling; machine learning; molecular dynamics; pharmacophore; virtualzzm321990screening

Mesh:

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Year:  2017        PMID: 28699534     DOI: 10.2174/1381612823666170710150604

Source DB:  PubMed          Journal:  Curr Pharm Des        ISSN: 1381-6128            Impact factor:   3.116


  2 in total

1.  Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells.

Authors:  Shuyun He; Duancheng Zhao; Yanle Ling; Hanxuan Cai; Yike Cai; Jiquan Zhang; Ling Wang
Journal:  Front Pharmacol       Date:  2021-12-17       Impact factor: 5.810

2.  Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics.

Authors:  Shraddha Parate; Vikas Kumar; Gihwan Lee; Shailima Rampogu; Jong Chan Hong; Keun Woo Lee
Journal:  Pharmaceuticals (Basel)       Date:  2021-03-21
  2 in total

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