Literature DB >> 21924376

Mobile apps for chemistry in the world of drug discovery.

Antony J Williams1, Sean Ekins, Alex M Clark, J James Jack, Richard L Apodaca.   

Abstract

Mobile hardware and software technology continues to evolve very rapidly and presents drug discovery scientists with new platforms for accessing data and performing data analysis. Smartphones and tablet computers can now be used to perform many of the operations previously addressed by laptops or desktop computers. Although the smaller screen sizes and requirements for touch-screen manipulation can present user-interface design challenges, especially with chemistry-related applications, these limitations are driving innovative solutions. In this early review of the topic, we collectively present our diverse experiences as software developer, chemistry database expert and naïve user, in terms of what mobile platforms could provide to the drug discovery chemist in the way of applications in the future as this disruptive technology takes off. Copyright Â
© 2011 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2011        PMID: 21924376     DOI: 10.1016/j.drudis.2011.09.002

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  12 in total

1.  App-etite for change.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2014-12-17       Impact factor: 3.686

2.  Making Transporter Models for Drug-Drug Interaction Prediction Mobile.

Authors:  Sean Ekins; Alex M Clark; Stephen H Wright
Journal:  Drug Metab Dispos       Date:  2015-07-21       Impact factor: 3.922

3.  Human-Device Interaction in the Life Science Laboratory.

Authors:  Robert Söldner; Sophia Rheinländer; Tim Meyer; Michael Olszowy; Jonas Austerjost
Journal:  Adv Biochem Eng Biotechnol       Date:  2022       Impact factor: 2.768

4.  Why open drug discovery needs four simple rules for licensing data and models.

Authors:  Antony J Williams; John Wilbanks; Sean Ekins
Journal:  PLoS Comput Biol       Date:  2012-09-27       Impact factor: 4.475

5.  Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.

Authors:  Alex M Clark; Krishna Dole; Anna Coulon-Spektor; Andrew McNutt; George Grass; Joel S Freundlich; Robert C Reynolds; Sean Ekins
Journal:  J Chem Inf Model       Date:  2015-06-03       Impact factor: 4.956

6.  Ten simple rules of live tweeting at scientific conferences.

Authors:  Sean Ekins; Ethan O Perlstein
Journal:  PLoS Comput Biol       Date:  2014-08-21       Impact factor: 4.475

7.  Redefining Cheminformatics with Intuitive Collaborative Mobile Apps.

Authors:  Alex M Clark; Sean Ekins; Antony J Williams
Journal:  Mol Inform       Date:  2012-07-04       Impact factor: 3.353

8.  Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration.

Authors:  Sean Ekins; Alex M Clark; Antony J Williams
Journal:  Mol Inform       Date:  2012-08-06       Impact factor: 3.353

9.  TB Mobile: a mobile app for anti-tuberculosis molecules with known targets.

Authors:  Sean Ekins; Alex M Clark; Malabika Sarker
Journal:  J Cheminform       Date:  2013-03-06       Impact factor: 5.514

10.  New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.

Authors:  Alex M Clark; Malabika Sarker; Sean Ekins
Journal:  J Cheminform       Date:  2014-08-04       Impact factor: 5.514

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