Literature DB >> 24283973

Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space.

Maria Alvim-Gaston, Timothy Grese, Abdelaziz Mahoui, Alan D Palkowitz, Marta Pineiro-Nunez, Ian Watson1.   

Abstract

The continued development of computational and synthetic methods has enabled the enumeration or preparation of a nearly endless universe of chemical structures. Nevertheless, the ability of this chemical universe to deliver small molecules that can both modulate biological targets and have drug-like physicochemical properties continues to be a topic of interest to the pharmaceutical industry and academic researchers alike. The chemical space described by public, commercial, in-house and virtual compound collections has been interrogated by multiple approaches including biochemical, cellular and virtual screening, diversity analysis, and in-silico profiling. However, current drugs and known chemical probes derived from these efforts are contained within a remarkably small volume of the predicted chemical space. Access to more diverse classes of chemical scaffolds that maintain the properties relevant for drug discovery is certainly needed to meet the increasing demands for pharmaceutical innovation. The Lilly Open Innovation Drug Discovery platform (OIDD) was designed to tackle barriers to innovation through the identification of novel molecules active in relevant disease biology models. In this article we will discuss several computational approaches towards describing novel, biologically active, drug-like chemical space and illustrate how the OIDD program may facilitate access to previously untapped molecules that may aid in the search for innovative pharmaceuticals.

Mesh:

Year:  2014        PMID: 24283973     DOI: 10.2174/1568026613666131127125858

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  12 in total

1.  BCSearch: fast structural fragment mining over large collections of protein structures.

Authors:  Frédéric Guyon; François Martz; Marek Vavrusa; Jérôme Bécot; Julien Rey; Pierre Tufféry
Journal:  Nucleic Acids Res       Date:  2015-05-14       Impact factor: 16.971

Review 2.  Powered by Open Innovation: Opportunities and Challenges in the Pharma Sector.

Authors:  Maria Angeles Martinez-Grau; Maria Alvim-Gaston
Journal:  Pharmaceut Med       Date:  2019-06

3.  Enantioselective synthesis and selective functionalization of 4-aminotetrahydroquinolines as novel GLP-1 secretagogues.

Authors:  Mustafa Z Kazancioglu; Kevin Quirion; Peter Wipf; Erin M Skoda
Journal:  Chirality       Date:  2021-12-28       Impact factor: 2.437

4.  Novel Phenotypic Outcomes Identified for a Public Collection of Approved Drugs from a Publicly Accessible Panel of Assays.

Authors:  Jonathan A Lee; Paul Shinn; Susan Jaken; Sarah Oliver; Francis S Willard; Steven Heidler; Robert B Peery; Jennifer Oler; Shaoyou Chu; Noel Southall; Thomas S Dexheimer; Jeffrey Smallwood; Ruili Huang; Rajarshi Guha; Ajit Jadhav; Karen Cox; Christopher P Austin; Anton Simeonov; G Sitta Sittampalam; Saba Husain; Natalie Franklin; David J Wild; Jeremy J Yang; Jeffrey J Sutherland; Craig J Thomas
Journal:  PLoS One       Date:  2015-07-15       Impact factor: 3.240

Review 5.  Open innovation and external sources of innovation. An opportunity to fuel the R&D pipeline and enhance decision making?

Authors:  Alexander Schuhmacher; Oliver Gassmann; Nigel McCracken; Markus Hinder
Journal:  J Transl Med       Date:  2018-05-08       Impact factor: 5.531

Review 6.  Changing Trends in Computational Drug Repositioning.

Authors:  Jaswanth K Yella; Suryanarayana Yaddanapudi; Yunguan Wang; Anil G Jegga
Journal:  Pharmaceuticals (Basel)       Date:  2018-06-05

7.  The creation and characterisation of a National Compound Collection: the Royal Society of Chemistry pilot.

Authors:  David M Andrews; Laura M Broad; Paul J Edwards; David N A Fox; Timothy Gallagher; Stephen L Garland; Richard Kidd; Joseph B Sweeney
Journal:  Chem Sci       Date:  2016-02-23       Impact factor: 9.825

8.  DaReUS-Loop: a web server to model multiple loops in homology models.

Authors:  Yasaman Karami; Julien Rey; Guillaume Postic; Samuel Murail; Pierre Tufféry; Sjoerd J de Vries
Journal:  Nucleic Acids Res       Date:  2019-07-02       Impact factor: 16.971

Review 9.  Towards reproducible computational drug discovery.

Authors:  Nalini Schaduangrat; Samuel Lampa; Saw Simeon; Matthew Paul Gleeson; Ola Spjuth; Chanin Nantasenamat
Journal:  J Cheminform       Date:  2020-01-28       Impact factor: 5.514

10.  DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins.

Authors:  Yasaman Karami; Frédéric Guyon; Sjoerd De Vries; Pierre Tufféry
Journal:  Sci Rep       Date:  2018-09-12       Impact factor: 4.379

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