Literature DB >> 23020747

Pfizer Global Virtual Library (PGVL): a chemistry design tool powered by experimentally validated parallel synthesis information.

Qiyue Hu1, Zhengwei Peng, Scott C Sutton, Jim Na, Jaroslav Kostrowicki, Bo Yang, Thomas Thacher, Xianjun Kong, Sarathy Mattaparti, Joe Zhongxiang Zhou, Javier Gonzalez, Michele Ramirez-Weinhouse, Atsuo Kuki.   

Abstract

An unprecedented amount of parallel synthesis information was accumulated within Pfizer over the past 12 years. This information was captured by an informatics tool known as PGVL (Pfizer Global Virtual Library). PGVL was used for many aspects of drug discovery including automated reactant mining and reaction product formation to build a synthetically feasible virtual compound collection. In this report, PGVL is discussed in detail. The chemistry information within PGVL has been used to extract synthesis and design information using an intuitive desktop Graphic User Interface, PGVL Hub. Several real-case examples of PGVL are also presented.

Mesh:

Year:  2012        PMID: 23020747     DOI: 10.1021/co300096q

Source DB:  PubMed          Journal:  ACS Comb Sci        ISSN: 2156-8944            Impact factor:   3.784


  6 in total

1.  Contemporary Computational Applications and Tools in Drug Discovery.

Authors:  Philip B Cox; Rishi Gupta
Journal:  ACS Med Chem Lett       Date:  2022-06-01       Impact factor: 4.632

Review 2.  Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design.

Authors:  Sebastjan Kralj; Marko Jukič; Urban Bren
Journal:  Int J Mol Sci       Date:  2021-12-30       Impact factor: 5.923

3.  LEADD: Lamarckian evolutionary algorithm for de novo drug design.

Authors:  Alan Kerstjens; Hans De Winter
Journal:  J Cheminform       Date:  2022-01-15       Impact factor: 5.514

Review 4.  Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design.

Authors:  Beatriz Suay-García; Jose I Bueso-Bordils; Antonio Falcó; Gerardo M Antón-Fos; Pedro A Alemán-López
Journal:  Int J Mol Sci       Date:  2022-01-30       Impact factor: 5.923

5.  Accelerating high-throughput virtual screening through molecular pool-based active learning.

Authors:  David E Graff; Eugene I Shakhnovich; Connor W Coley
Journal:  Chem Sci       Date:  2021-04-29       Impact factor: 9.825

6.  Generating Multibillion Chemical Space of Readily Accessible Screening Compounds.

Authors:  Oleksandr O Grygorenko; Dmytro S Radchenko; Igor Dziuba; Alexander Chuprina; Kateryna E Gubina; Yurii S Moroz
Journal:  iScience       Date:  2020-10-15
  6 in total

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