Literature DB >> 17973472

Advanced biological and chemical discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world.

Dimitris K Agrafiotis1, Simson Alex, Heng Dai, An Derkinderen, Michael Farnum, Peter Gates, Sergei Izrailev, Edward P Jaeger, Paul Konstant, Albert Leung, Victor S Lobanov, Patrick Marichal, Douglas Martin, Dmitrii N Rassokhin, Maxim Shemanarev, Andrew Skalkin, John Stong, Tom Tabruyn, Marleen Vermeiren, Jackson Wan, Xiang Yang Xu, Xiang Yao.   

Abstract

We present ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. ABCD is an attempt to bridge multiple continents, data systems, and cultures using modern information technology and to provide scientists with tools that allow them to analyze multifactorial SAR and make informed, data-driven decisions. The system consists of three major components: (1) a data warehouse, which combines data from multiple chemical and pharmacological transactional databases, designed for supreme query performance; (2) a state-of-the-art application suite, which facilitates data upload, retrieval, mining, and reporting, and (3) a workspace, which facilitates collaboration and data sharing by allowing users to share queries, templates, results, and reports across project teams, campuses, and other organizational units. Chemical intelligence, performance, and analytical sophistication lie at the heart of the new system, which was developed entirely in-house. ABCD is used routinely by more than 1000 scientists around the world and is rapidly expanding into other functional areas within the J&J organization.

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Mesh:

Year:  2007        PMID: 17973472     DOI: 10.1021/ci700267w

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  14 in total

1.  Broadening access to electronic healthcare databases.

Authors:  M Soledad Cepeda; Victor S Lobanov; Michael Farnum; Rachel Weinstein; Peter Gates; Dimitris K Agrafiotis; Paul Stang; Jesse A Berlin
Journal:  Nat Rev Drug Discov       Date:  2010-01       Impact factor: 84.694

2.  Computational chemistry at Janssen.

Authors:  Herman van Vlijmen; Renee L Desjarlais; Tara Mirzadegan
Journal:  J Comput Aided Mol Des       Date:  2016-12-19       Impact factor: 3.686

Review 3.  An Overview of the Challenges in Designing, Integrating, and Delivering BARD: A Public Chemical-Biology Resource and Query Portal for Multiple Organizations, Locations, and Disciplines.

Authors:  Andrea de Souza; Joshua A Bittker; David L Lahr; Steve Brudz; Simon Chatwin; Tudor I Oprea; Anna Waller; Jeremy J Yang; Noel Southall; Rajarshi Guha; Stephan C Schürer; Uma D Vempati; Mark R Southern; Eric S Dawson; Paul A Clemons; Thomas D Y Chung
Journal:  J Biomol Screen       Date:  2014-01-17

Review 4.  Reporting biological assay screening results for maximum impact.

Authors:  Evan Bolton
Journal:  Drug Discov Today Technol       Date:  2015-05-02

5.  An informatic pipeline for managing high-throughput screening experiments and analyzing data from stereochemically diverse libraries.

Authors:  Carol A Mulrooney; David L Lahr; Michael J Quintin; Willmen Youngsaye; Dennis Moccia; Jacob K Asiedu; Evan L Mulligan; Lakshmi B Akella; Lisa A Marcaurelle; Philip Montgomery; Joshua A Bittker; Paul A Clemons; Stephen Brudz; Sivaraman Dandapani; Jeremy R Duvall; Nicola J Tolliday; Andrea De Souza
Journal:  J Comput Aided Mol Des       Date:  2013-04-13       Impact factor: 3.686

6.  CheS-Mapper - Chemical Space Mapping and Visualization in 3D.

Authors:  Martin Gütlein; Andreas Karwath; Stefan Kramer
Journal:  J Cheminform       Date:  2012-03-17       Impact factor: 5.514

7.  A late-binding, distributed, NoSQL warehouse for integrating patient data from clinical trials.

Authors:  Eric Yang; Jeremy D Scheff; Shih C Shen; Michael A Farnum; James Sefton; Victor S Lobanov; Dimitris K Agrafiotis
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

Review 8.  Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research.

Authors:  Laurianne David; Josep Arús-Pous; Johan Karlsson; Ola Engkvist; Esben Jannik Bjerrum; Thierry Kogej; Jan M Kriegl; Bernd Beck; Hongming Chen
Journal:  Front Pharmacol       Date:  2019-11-05       Impact factor: 5.810

9.  A self-organizing algorithm for modeling protein loops.

Authors:  Pu Liu; Fangqiang Zhu; Dmitrii N Rassokhin; Dimitris K Agrafiotis
Journal:  PLoS Comput Biol       Date:  2009-08-21       Impact factor: 4.475

10.  VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.

Authors:  Bence Bolgár; Péter Antal
Journal:  BMC Bioinformatics       Date:  2017-10-04       Impact factor: 3.169

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