Literature DB >> 30108824

High throughput methods to measure the propensity of compounds to form intramolecular hydrogen bonding.

Giulia Caron1, Maura Vallaro1, Giuseppe Ermondi1.   

Abstract

Implementation of IMHB considerations in drug discovery needs robust and validated descriptors to experimentally verify the propensity of compounds to exhibit IMHBs. The first part of the paper presents an overview of the most common techniques to measure the propensity of compounds to form IMHBs. Then we review and discuss recently proposed high throughput (HT) physicochemical descriptors (i.e. Δlog Poct-tol, EPSA and log k'80 PLRP-S) which provide the same information. Analysis of the available data enabled us to extract guidelines for the application of these descriptors in drug discovery programs.

Entities:  

Year:  2017        PMID: 30108824      PMCID: PMC6071820          DOI: 10.1039/c7md00101k

Source DB:  PubMed          Journal:  Medchemcomm        ISSN: 2040-2503            Impact factor:   3.597


  43 in total

1.  Combined molecular lipophilicity descriptors and their role in understanding intramolecular effects.

Authors: 
Journal:  Pharm Sci Technolo Today       Date:  1999-08

2.  ElogD(oct): a tool for lipophilicity determination in drug discovery. 2. Basic and neutral compounds.

Authors:  F Lombardo; M Y Shalaeva; K A Tupper; F Gao
Journal:  J Med Chem       Date:  2001-07-19       Impact factor: 7.446

3.  Conformational flexibility, internal hydrogen bonding, and passive membrane permeability: successful in silico prediction of the relative permeabilities of cyclic peptides.

Authors:  Taha Rezai; Jonathan E Bock; Mai V Zhou; Chakrapani Kalyanaraman; R Scott Lokey; Matthew P Jacobson
Journal:  J Am Chem Soc       Date:  2006-11-01       Impact factor: 15.419

Review 4.  Supercritical fluid chromatography in pharmaceutical analysis.

Authors:  Vincent Desfontaine; Davy Guillarme; Eric Francotte; Lucie Nováková
Journal:  J Pharm Biomed Anal       Date:  2015-03-14       Impact factor: 3.935

5.  Relationship between Passive Permeability and Molecular Polarity Using Block Relevance Analysis.

Authors:  Gilles H Goetz; Marina Shalaeva; Giulia Caron; Giuseppe Ermondi; Laurence Philippe
Journal:  Mol Pharm       Date:  2017-01-17       Impact factor: 4.939

6.  Nanoscale measurement of the dielectric constant of supported lipid bilayers in aqueous solutions with electrostatic force microscopy.

Authors:  G Gramse; A Dols-Perez; M A Edwards; L Fumagalli; G Gomila
Journal:  Biophys J       Date:  2013-03-19       Impact factor: 4.033

7.  Hydrogen Bond Basicity Prediction for Medicinal Chemistry Design.

Authors:  Peter W Kenny; Carlos A Montanari; Igor M Prokopczyk; Jean F R Ribeiro; Geraldo Rodrigues Sartori
Journal:  J Med Chem       Date:  2016-02-26       Impact factor: 7.446

8.  Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs.

Authors:  Adrian Whitty; Mengqi Zhong; Lauren Viarengo; Dmitri Beglov; David R Hall; Sandor Vajda
Journal:  Drug Discov Today       Date:  2016-02-15       Impact factor: 7.851

9.  An NMR method for the quantitative assessment of intramolecular hydrogen bonding; application to physicochemical, environmental, and biochemical properties.

Authors:  Michael H Abraham; Raymond J Abraham; William E Acree; Abil E Aliev; Al J Leo; William L Whaley
Journal:  J Org Chem       Date:  2014-11-11       Impact factor: 4.354

Review 10.  Application of high-performance liquid chromatography based measurements of lipophilicity to model biological distribution.

Authors:  Klára Valkó
Journal:  J Chromatogr A       Date:  2004-05-28       Impact factor: 4.759

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  2 in total

1.  Steering New Drug Discovery Campaigns: Permeability, Solubility, and Physicochemical Properties in the bRo5 Chemical Space.

Authors:  Giulia Caron; Jan Kihlberg; Gilles Goetz; Ekaterina Ratkova; Vasanthanathan Poongavanam; Giuseppe Ermondi
Journal:  ACS Med Chem Lett       Date:  2021-01-05       Impact factor: 4.345

2.  Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies.

Authors:  Christoph A Bauer; Gisbert Schneider; Andreas H Göller
Journal:  J Cheminform       Date:  2019-09-11       Impact factor: 5.514

  2 in total

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