Literature DB >> 27124799

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia.

György M Keserű1, Daniel A Erlanson2, György G Ferenczy1, Michael M Hann3, Christopher W Murray4, Stephen D Pickett3.   

Abstract

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation, and screening technologies. This compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia.

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Year:  2016        PMID: 27124799     DOI: 10.1021/acs.jmedchem.6b00197

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  43 in total

Review 1.  Expanding the medicinal chemistry synthetic toolbox.

Authors:  Jonas Boström; Dean G Brown; Robert J Young; György M Keserü
Journal:  Nat Rev Drug Discov       Date:  2018-08-24       Impact factor: 84.694

Review 2.  Twenty years on: the impact of fragments on drug discovery.

Authors:  Daniel A Erlanson; Stephen W Fesik; Roderick E Hubbard; Wolfgang Jahnke; Harren Jhoti
Journal:  Nat Rev Drug Discov       Date:  2016-07-15       Impact factor: 84.694

Review 3.  An outlook on using serial femtosecond crystallography in drug discovery.

Authors:  Alexey Mishin; Anastasiia Gusach; Aleksandra Luginina; Egor Marin; Valentin Borshchevskiy; Vadim Cherezov
Journal:  Expert Opin Drug Discov       Date:  2019-06-11       Impact factor: 6.098

4.  Cocktailed fragment screening by X-ray crystallography of the antibacterial target undecaprenyl pyrophosphate synthase from Acinetobacter baumannii.

Authors:  James H Thorpe; Ian D Wall; Robert H Sinnamon; Amy N Taylor; Robert A Stavenger
Journal:  Acta Crystallogr F Struct Biol Commun       Date:  2020-01-01       Impact factor: 1.056

5.  Determination of ligand binding modes in weak protein-ligand complexes using sparse NMR data.

Authors:  Biswaranjan Mohanty; Martin L Williams; Bradley C Doak; Mansha Vazirani; Olga Ilyichova; Geqing Wang; Wolfgang Bermel; Jamie S Simpson; David K Chalmers; Glenn F King; Mehdi Mobli; Martin J Scanlon
Journal:  J Biomol NMR       Date:  2016-10-24       Impact factor: 2.835

6.  Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing.

Authors:  E Sila Ozdemir; Farideh Halakou; Ruth Nussinov; Attila Gursoy; Ozlem Keskin
Journal:  Methods Mol Biol       Date:  2019

7.  Protein-Observed Fluorine NMR Is a Complementary Ligand Discovery Method to 1H CPMG Ligand-Observed NMR.

Authors:  Andrew K Urick; Luis Pablo Calle; Juan F Espinosa; Haitao Hu; William C K Pomerantz
Journal:  ACS Chem Biol       Date:  2016-10-05       Impact factor: 5.100

Review 8.  Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications.

Authors:  Yuemin Bian; Xiang-Qun Sean Xie
Journal:  AAPS J       Date:  2018-04-09       Impact factor: 4.009

9.  Quantitative Irreversible Tethering (qIT) for Target-directed Covalent Fragment Screening.

Authors:  Gregory B Craven; Alan Armstrong; David J Mann
Journal:  Bio Protoc       Date:  2020-12-20

10.  19F-Tagged metal binding pharmacophores for NMR screening of metalloenzymes.

Authors:  Kathleen E Prosser; Alysia J Kohlbrand; Hyeonglim Seo; Mark Kalaj; Seth M Cohen
Journal:  Chem Commun (Camb)       Date:  2021-05-18       Impact factor: 6.222

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