Literature DB >> 20683645

Chemical space: missing pieces in cheminformatics.

Sean Ekins1, Rishi R Gupta, Eric Gifford, Barry A Bunin, Chris L Waller.   

Abstract

Cheminformatics is at a turning point, the pharmaceutical industry benefits from using the various methods developed over the last twenty years, but in our opinion we need to see greater development of novel approaches that non-experts can use. This will be achieved by more collaborations between software companies, academics and the evolving pharmaceutical industry. We suggest that cheminformatics should also be looking to other industries that use high performance computing technologies for inspiration. We describe the needs and opportunities which may benefit from the development of open cheminformatics technologies, mobile computing, the movement of software to the cloud and precompetitive initiatives.

Mesh:

Year:  2010        PMID: 20683645     DOI: 10.1007/s11095-010-0229-0

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.200


  34 in total

Review 1.  ADMET in silico modelling: towards prediction paradise?

Authors:  Han van de Waterbeemd; Eric Gifford
Journal:  Nat Rev Drug Discov       Date:  2003-03       Impact factor: 84.694

2.  Implementation of an ADME enabling selection and visualization tool for drug discovery.

Authors:  Chad L Stoner; Eric Gifford; Charles Stankovic; Christopher S Lepsy; Joanne Brodfuehrer; J V N Vara Prasad; Narayanan Surendran
Journal:  J Pharm Sci       Date:  2004-05       Impact factor: 3.534

Review 3.  3D database searching in drug design.

Authors:  Y C Martin
Journal:  J Med Chem       Date:  1992-06-12       Impact factor: 7.446

Review 4.  Application of data mining approaches to drug delivery.

Authors:  Sean Ekins; Jun Shimada; Cheng Chang
Journal:  Adv Drug Deliv Rev       Date:  2006-09-22       Impact factor: 15.470

5.  Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

Authors:  Hao Zhu; Alexander Tropsha; Denis Fourches; Alexandre Varnek; Ester Papa; Paola Gramatica; Tomas Oberg; Phuong Dao; Artem Cherkasov; Igor V Tetko
Journal:  J Chem Inf Model       Date:  2008-03-01       Impact factor: 4.956

6.  Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups.

Authors:  James T Metz; Jeffrey R Huth; Philip J Hajduk
Journal:  J Comput Aided Mol Des       Date:  2007-03-06       Impact factor: 3.686

7.  Novel chemical space exploration via natural products.

Authors:  Josefin Rosén; Johan Gottfries; Sorel Muresan; Anders Backlund; Tudor I Oprea
Journal:  J Med Chem       Date:  2009-04-09       Impact factor: 7.446

Review 8.  Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery.

Authors:  Michael R Barnes; Lee Harland; Steven M Foord; Matthew D Hall; Ian Dix; Scott Thomas; Bryn I Williams-Jones; Cory R Brouwer
Journal:  Nat Rev Drug Discov       Date:  2009-07-17       Impact factor: 84.694

9.  Identification of common functional configurations among molecules.

Authors:  D Barnum; J Greene; A Smellie; P Sprague
Journal:  J Chem Inf Comput Sci       Date:  1996 May-Jun

10.  Integrated oral bioavailability projection using in vitro screening data as a selection tool in drug discovery.

Authors:  Chad L Stoner; Adriaan Cleton; Kjell Johnson; Doo-Man Oh; Hussein Hallak; Joanne Brodfuehrer; Narayanan Surendran; Hyo-Kyung Han
Journal:  Int J Pharm       Date:  2004-01-09       Impact factor: 5.875

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  10 in total

1.  Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis.

Authors:  Sean Ekins; Allen C Casey; David Roberts; Tanya Parish; Barry A Bunin
Journal:  Tuberculosis (Edinb)       Date:  2013-12-19       Impact factor: 3.131

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

Review 3.  The Next Era: Deep Learning in Pharmaceutical Research.

Authors:  Sean Ekins
Journal:  Pharm Res       Date:  2016-09-06       Impact factor: 4.200

4.  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

5.  Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis.

Authors:  Sean Ekins; Joel S Freundlich; Robert C Reynolds
Journal:  J Chem Inf Model       Date:  2014-07-17       Impact factor: 4.956

6.  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

7.  Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.

Authors:  Sean Ekins; Joel S Freundlich; Robert C Reynolds
Journal:  J Chem Inf Model       Date:  2013-10-30       Impact factor: 4.956

8.  Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.

Authors:  Sean Ekins; Joel S Freundlich; Judith V Hobrath; E Lucile White; Robert C Reynolds
Journal:  Pharm Res       Date:  2013-10-17       Impact factor: 4.200

Review 9.  Thymic function in the regulation of T cells, and molecular mechanisms underlying the modulation of cytokines and stress signaling (Review).

Authors:  Fenggen Yan; Xiumei Mo; Junfeng Liu; Siqi Ye; Xing Zeng; Dacan Chen
Journal:  Mol Med Rep       Date:  2017-09-19       Impact factor: 2.952

10.  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

  10 in total

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