Literature DB >> 23568475

Computational models for tuberculosis drug discovery.

Sean Ekins1, Joel S Freundlich.   

Abstract

The search for small molecules with activity against Mycobacterium tuberculosis increasingly uses -high-throughput screening and computational methods. Previously, we have analyzed recent studies in which computational tools were used for cheminformatics. We have now updated this analysis to illustrate how they may assist in finding desirable leads for tuberculosis drug discovery. We provide our thoughts on strategies for drug discovery efforts for neglected diseases.

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Year:  2013        PMID: 23568475     DOI: 10.1007/978-1-62703-342-8_16

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  14 in total

1.  Computational models for neglected diseases: gaps and opportunities.

Authors:  Elizabeth L Ponder; Joel S Freundlich; Malabika Sarker; Sean Ekins
Journal:  Pharm Res       Date:  2013-08-30       Impact factor: 4.200

2.  Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

Authors:  Janaina Cruz Pereira; Samer S Daher; Kimberley M Zorn; Matthew Sherwood; Riccardo Russo; Alexander L Perryman; Xin Wang; Madeleine J Freundlich; Sean Ekins; Joel S Freundlich
Journal:  Pharm Res       Date:  2020-07-13       Impact factor: 4.200

3.  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 4.  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

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.  Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

Authors:  Alexander L Perryman; Thomas P Stratton; Sean Ekins; Joel S Freundlich
Journal:  Pharm Res       Date:  2015-09-28       Impact factor: 4.200

7.  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

8.  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

9.  Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus.

Authors:  Jimmy S Patel; Javiera Norambuena; Hassan Al-Tameemi; Yong-Mo Ahn; Alexander L Perryman; Xin Wang; Samer S Daher; James Occi; Riccardo Russo; Steven Park; Matthew Zimmerman; Hsin-Pin Ho; David S Perlin; Véronique Dartois; Sean Ekins; Pradeep Kumar; Nancy Connell; Jeffrey M Boyd; Joel S Freundlich
Journal:  ACS Infect Dis       Date:  2021-08-03       Impact factor: 5.578

10.  Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis.

Authors:  Sean Ekins; Richard Pottorf; Robert C Reynolds; Antony J Williams; Alex M Clark; Joel S Freundlich
Journal:  J Chem Inf Model       Date:  2014-04-03       Impact factor: 4.956

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