Covalent docking of large libraries for the discovery of chemical probes

London, Nir; Miller, Rand M.; Krishnan, Shyam; Uchida, Kenji; Irwin, John J.; Eidam, O.; Gibold, Lucie; Cimermančič, Peter; Bonnet, Richard; Shoichet, Brian K.; Taunton, Jack

doi:10.1038/nchembio.1666

Cited by 236 publications

(296 citation statements)

References 58 publications

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“…Subsequently, the ligand link atom is overlaid on the protein link atom and the geometry of the covalent bond is evaluated by specific terms of the scoring function (clash, torsion and valence-angle bending terms). Another program-DOCKovalent-is an adaptation of DOCK3.6 aimed to perform large-scale, covalent virtual screening [95]. The algorithm defines a priori a covalent attachment point and systematically explores the ligand conformational space around the modeled covalent bond.…”

Section: Covalent Bonds In Molecular Dockingmentioning

confidence: 99%

Molecular Docking and Structure-Based Drug Design Strategies

et al. 2015

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Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure-and ligand-based methods.

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Section: Covalent Bonds In Molecular Dockingmentioning

confidence: 99%

Molecular Docking and Structure-Based Drug Design Strategies

et al. 2015

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“…Our results provide a blueprint for such an inhibitor discovery strategy. Such a strategy will be facilitated further by the recent publication of freely available, web-based protocols for covalent docking [25] and virtual screening [30].…”

Section: F E B Amentioning

confidence: 99%

Covalent inhibitors of LgtC: A blueprint for the discovery of non-substrate-like inhibitors for bacterial glycosyltransferases

Smith

Vivoli

et al. 2017

Bioorganic & Medicinal Chemistry

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. Covalent inhibitors of LgtC: a blueprint for the discovery of non-substrate-like inhibitors for bacterial glycosyltransferases. Bioorganic and Medicinal Chemistry, 25(12), 3182-3194. https://doi.org/10.1016/j.bmc.2017.04.006Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policyIf you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.-1 - data reveals that non-catalytic cysteines are a common motif in the active site of many bacterial glycosyltransferases. Our results can therefore serve as a blueprint for the rational design of non-substrate-like, covalent inhibitors against a broad range of other bacterial glycosyltransferases.-3 -

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“…Virtual screening methods are widely used nowadays in the drug discovery process (Zhao et al, 2013;Ma et al, 2011;Yan et al, 2014;London et al, 2014), where they provide with predictions about which ligands from large compound databases might bind to certain protein targets. Using this approach,…”

Section: Introductionmentioning

confidence: 99%

Automatic selection of molecular descriptors using random forest: Application to drug discovery

Cano

García-Rodríguez

García-García

et al. 2017

Expert Systems with Applications

102

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The optimal selection of chemical features (molecular descriptors) is an essential pre-processing step for the efficient application of computational intelligence techniques in virtual screening for identification of bioactive molecules in drug discovery. The selection of molecular descriptors has key influence in the accuracy of affinity prediction. In order to improve this prediction, we examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes. The reduction of features to use during prediction dramatically reduces the computing time over existing approaches and consequently permits the exploration of much larger sets of experimental data. To test the validity of the method, we compared the results of our approach with the ones obtained using manual feature selection in our previous study (Perez-Sanchez et al., 2014).The main novelty of this work in the field of drug discovery is the use of RF in two different ways: feature ranking and dimensionality reduction, and classification * Corresponding author: Tel.: +34 610488989; fax: +34 965903681Email addresses: gcano@dtic.ua.es (Gaspar Cano), jgr@ua.es (Jose Garcia-Rodriguez), agarcia@dtic.ua.es (Alberto Garcia-Garcia), hperez@ucam.edu (Horacio Perez-Sanchez), benedikt@hi.is (Jón Atli Benediktsson), anilth@hi.is (Anil Thapa), A.Barr1@westminster.ac.uk (Alastair Barr)

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Covalent docking of large libraries for the discovery of chemical probes

Cited by 236 publications

References 58 publications

Molecular Docking and Structure-Based Drug Design Strategies

Molecular Docking and Structure-Based Drug Design Strategies

Covalent inhibitors of LgtC: A blueprint for the discovery of non-substrate-like inhibitors for bacterial glycosyltransferases

Automatic selection of molecular descriptors using random forest: Application to drug discovery

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