Literature DB >> 20024044

Precompetitive preclinical ADME/Tox data: set it free on the web to facilitate computational model building and assist drug development.

Sean Ekins1, Antony J Williams.   

Abstract

Web-based technologies coupled with a drive for improved communication between scientists have resulted in the proliferation of scientific opinion, data and knowledge at an ever-increasing rate. The increasing array of chemistry-related computer-based resources now available provides chemists with a direct path to the discovery of information, once previously accessed via library services and limited to commercial and costly resources. We propose that preclinical absorption, distribution, metabolism, excretion and toxicity data as well as pharmacokinetic properties from studies published in the literature (which use animal or human tissues in vitro or from in vivo studies) are precompetitive in nature and should be freely available on the web. This could be made possible by curating the literature and patents, data donations from pharmaceutical companies and by expanding the currently freely available ChemSpider database of over 21 million molecules with physicochemical properties. This will require linkage to PubMed, PubChem and Wikipedia as well as other frequently used public databases that are currently used, mining the full text publications to extract the pertinent experimental data. These data will need to be extracted using automated and manual methods, cleaned and then published to the ChemSpider or other database such that it will be freely available to the biomedical research and clinical communities. The value of the data being accessible will improve development of drug molecules with good ADME/Tox properties, facilitate computational model building for these properties and enable researchers to not repeat the failures of past drug discovery studies.

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Year:  2009        PMID: 20024044     DOI: 10.1039/b917760b

Source DB:  PubMed          Journal:  Lab Chip        ISSN: 1473-0189            Impact factor:   6.799


  18 in total

1.  Chemical space: missing pieces in cheminformatics.

Authors:  Sean Ekins; Rishi R Gupta; Eric Gifford; Barry A Bunin; Chris L Waller
Journal:  Pharm Res       Date:  2010-08-04       Impact factor: 4.200

Review 2.  Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).

Authors:  Sean Ekins; Anna Coulon Spektor; Alex M Clark; Krishna Dole; Barry A Bunin
Journal:  Drug Discov Today       Date:  2016-11-22       Impact factor: 7.851

3.  Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Authors:  Alexandru Korotcov; Valery Tkachenko; Daniel P Russo; Sean Ekins
Journal:  Mol Pharm       Date:  2017-11-13       Impact factor: 4.939

4.  A substrate pharmacophore for the human organic cation/carnitine transporter identifies compounds associated with rhabdomyolysis.

Authors:  Sean Ekins; Lei Diao; James E Polli
Journal:  Mol Pharm       Date:  2012-02-28       Impact factor: 4.939

5.  Exploiting PubChem for Virtual Screening.

Authors:  Xiang-Qun Xie
Journal:  Expert Opin Drug Discov       Date:  2010-12       Impact factor: 6.098

Review 6.  Modeling and predicting clinical efficacy for drugs targeting the tumor milieu.

Authors:  Mallika Singh; Napoleone Ferrara
Journal:  Nat Biotechnol       Date:  2012-07-10       Impact factor: 54.908

7.  Opportunities and challenges using artificial intelligence in ADME/Tox.

Authors:  Barun Bhhatarai; W Patrick Walters; Cornelis E C A Hop; Guido Lanza; Sean Ekins
Journal:  Nat Mater       Date:  2019-05       Impact factor: 43.841

8.  Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening.

Authors:  Liying Zhang; Denis Fourches; Alexander Sedykh; Hao Zhu; Alexander Golbraikh; Sean Ekins; Julie Clark; Michele C Connelly; Martina Sigal; Dena Hodges; Armand Guiguemde; R Kiplin Guy; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2013-01-23       Impact factor: 4.956

9.  Bigger data, collaborative tools and the future of predictive drug discovery.

Authors:  Sean Ekins; Alex M Clark; S Joshua Swamidass; Nadia Litterman; Antony J Williams
Journal:  J Comput Aided Mol Des       Date:  2014-06-19       Impact factor: 3.686

10.  A Database Developed with Information Extracted from Chemotherapy Drug Package Inserts to Enhance Future Prescriptions.

Authors:  Malcolm J D'Souza; Ghada J Alabed; Jordan M Wheatley; Natalia Roberts; Yogasudha Veturi; Xia Bi; Christopher Hart Continisio
Journal:  Conf Comput Vis Pattern Recognit Workshops       Date:  2011
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