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Literature DB >> 23568469

The Collaborative Drug Discovery (CDD) database.

Abstract

The broad goals of Collaborative Drug Discovery (CDD) are to enable a collaborative "cloud-based" tool to be used to bring together neglected disease researchers and other researchers from usually separate areas, to collaborate and to share compounds and drug discovery data in the research community, which will ultimately result in long-term improvements in the research enterprise and health care delivery. This chapter briefly introduces CDD software and describes applications in antimalarial and tuberculosis research.

Entities: Disease

Mesh：

Substances：

Year: 2013 PMID： 23568469 DOI： 10.1007/978-1-62703-342-8_10

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

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Cited

12 in total

1. D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

Authors: Zied Gaieb; Shuai Liu; Symon Gathiaka; Michael Chiu; Huanwang Yang; Chenghua Shao; Victoria A Feher; W Patrick Walters; Bernd Kuhn; Markus G Rudolph; Stephen K Burley; Michael K Gilson; Rommie E Amaro
Journal: J Comput Aided Mol Des Date: 2017-12-04 Impact factor: 3.686

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. D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.

Authors: Symon Gathiaka; Shuai Liu; Michael Chiu; Huanwang Yang; Jeanne A Stuckey; You Na Kang; Jim Delproposto; Ginger Kubish; James B Dunbar; Heather A Carlson; Stephen K Burley; W Patrick Walters; Rommie E Amaro; Victoria A Feher; Michael K Gilson
Journal: J Comput Aided Mol Des Date: 2016-09-30 Impact factor: 3.686

4. 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

5. Collaboration for rare disease drug discovery research.

Authors: Nadia K Litterman; Michele Rhee; David C Swinney; Sean Ekins
Journal: F1000Res Date: 2014-10-31

6. Thiophenecarboxamide Derivatives Activated by EthA Kill Mycobacterium tuberculosis by Inhibiting the CTP Synthetase PyrG.

Authors: Giorgia Mori; Laurent R Chiarelli; Marta Esposito; Vadim Makarov; Marco Bellinzoni; Ruben C Hartkoorn; Giulia Degiacomi; Francesca Boldrin; Sean Ekins; Ana Luisa de Jesus Lopes Ribeiro; Leonardo B Marino; Ivana Centárová; Zuzana Svetlíková; Jaroslav Blaško; Elena Kazakova; Alexander Lepioshkin; Nathalie Barilone; Giuseppe Zanoni; Alessio Porta; Marco Fondi; Renato Fani; Alain R Baulard; Katarína Mikušová; Pedro M Alzari; Riccardo Manganelli; Luiz Pedro S de Carvalho; Giovanna Riccardi; Stewart T Cole; Maria Rosalia Pasca
Journal: Chem Biol Date: 2015-06-18

The Collaborative Drug Discovery (CDD) database.

1. D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

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

3. D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.

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

5. Collaboration for rare disease drug discovery research.

6. Thiophenecarboxamide Derivatives Activated by EthA Kill Mycobacterium tuberculosis by Inhibiting the CTP Synthetase PyrG.

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

8. Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses.

Review 9. Review of Drug Repositioning Approaches and Resources.

Review 10. Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.