Literature DB >> 23990313

Computational models for neglected diseases: gaps and opportunities.

Elizabeth L Ponder1, Joel S Freundlich, Malabika Sarker, Sean Ekins.   

Abstract

Neglected diseases, such as Chagas disease, African sleeping sickness, and intestinal worms, affect millions of the world's poor. They disproportionately affect marginalized populations, lack effective treatments or vaccines, or existing products are not accessible to the populations affected. Computational approaches have been used across many of these diseases for various aspects of research or development, and yet data produced by computational approaches are not integrated and widely accessible to others. Here, we identify gaps in which computational approaches have been used for some neglected diseases and not others. We also make recommendations for the broad-spectrum integration of these techniques into a neglected disease drug discovery and development workflow.

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Year:  2013        PMID: 23990313     DOI: 10.1007/s11095-013-1170-9

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


  79 in total

Review 1.  Informatics resources for tuberculosis--towards drug discovery.

Authors:  Jagadish Chandrabose Sundaramurthi; S Brindha; T B K Reddy; Luke Elizabeth Hanna
Journal:  Tuberculosis (Edinb)       Date:  2011-09-22       Impact factor: 3.131

2.  Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

Authors:  Yovani Marrero-Ponce; Maité Iyarreta-Veitía; Alina Montero-Torres; Carlos Romero-Zaldivar; Carlos A Brandt; Priscilla E Avila; Karin Kirchgatter; Yanetsy Machado
Journal:  J Chem Inf Model       Date:  2005 Jul-Aug       Impact factor: 4.956

3.  A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools.

Authors:  H-H Lin; I Langley; R Mwenda; B Doulla; S Egwaga; K A Millington; G H Mann; M Murray; S B Squire; T Cohen
Journal:  Int J Tuberc Lung Dis       Date:  2011-08       Impact factor: 2.373

4.  Efficient identification of inhibitors targeting the closed active site conformation of the HPRT from Trypanosoma cruzi.

Authors:  D M Freymann; M A Wenck; J C Engel; J Feng; P J Focia; A E Eakin; S P Craig
Journal:  Chem Biol       Date:  2000-12

5.  A collaborative database and computational models for tuberculosis drug discovery.

Authors:  Sean Ekins; Justin Bradford; Krishna Dole; Anna Spektor; Kellan Gregory; David Blondeau; Moses Hohman; Barry A Bunin
Journal:  Mol Biosyst       Date:  2010-02-09

6.  Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis.

Authors:  Sandeep K Kushwaha; Madhvi Shakya
Journal:  J Theor Biol       Date:  2009-10-13       Impact factor: 2.691

7.  Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis.

Authors:  Carola Huthmacher; Andreas Hoppe; Sascha Bulik; Hermann-Georg Holzhütter
Journal:  BMC Syst Biol       Date:  2010-08-31

8.  Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions.

Authors:  Yamilet Rodriguez-Soca; Cristian R Munteanu; Julián Dorado; Alejandro Pazos; Francisco J Prado-Prado; Humberto González-Díaz
Journal:  J Proteome Res       Date:  2010-02-05       Impact factor: 4.466

9.  Computational prediction of host-pathogen protein-protein interactions.

Authors:  Matthew D Dyer; T M Murali; Bruno W Sobral
Journal:  Bioinformatics       Date:  2007-07-01       Impact factor: 6.937

10.  Discovery of novel MDR-Mycobacterium tuberculosis inhibitor by new FRIGATE computational screen.

Authors:  Christoph Scheich; Zoltán Szabadka; Beáta Vértessy; Vera Pütter; Vince Grolmusz; Markus Schade
Journal:  PLoS One       Date:  2011-12-02       Impact factor: 3.240

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  13 in total

Review 1.  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

2.  A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules.

Authors:  Kimberley M Zorn; Shengxi Sun; Cecelia L McConnon; Kelley Ma; Eric K Chen; Daniel H Foil; Thomas R Lane; Lawrence J Liu; Nelly El-Sakkary; Danielle E Skinner; Sean Ekins; Conor R Caffrey
Journal:  ACS Infect Dis       Date:  2021-01-12       Impact factor: 5.084

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.  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

5.  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

6.  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

7.  Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.

Authors:  Sean Ekins; Jair Lage de Siqueira-Neto; Laura-Isobel McCall; Malabika Sarker; Maneesh Yadav; Elizabeth L Ponder; E Adam Kallel; Danielle Kellar; Steven Chen; Michelle Arkin; Barry A Bunin; James H McKerrow; Carolyn Talcott
Journal:  PLoS Negl Trop Dis       Date:  2015-06-26

8.  Finding new collaboration models for enabling neglected tropical disease drug discovery.

Authors:  Michael P Pollastri
Journal:  PLoS Negl Trop Dis       Date:  2014-07-03

Review 9.  Prediction of antischistosomal small molecules using machine learning in the era of big data.

Authors:  Samuel K Kwofie; Kwasi Agyenkwa-Mawuli; Emmanuel Broni; Whelton A Miller Iii; Michael D Wilson
Journal:  Mol Divers       Date:  2021-08-05       Impact factor: 2.943

10.  A computer-aided approach to identify novel Leishmania major protein disulfide isomerase inhibitors for treatment of leishmaniasis.

Authors:  Noureddine Ben Khalaf; Susie Pham; Giuseppe Romeo; Sara Abdelghany; Sebastiano Intagliata; Peter Sedillo; Loredana Salerno; Jessica Gonzales; Dahmani M Fathallah; Douglas J Perkins; Ivy Hurwitz; Valeria Pittalà
Journal:  J Comput Aided Mol Des       Date:  2021-02-22       Impact factor: 3.686

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