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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Balganesh TS, Alzari PM, Cole ST (2008) Rising standards for tuberculosis drug development. Trends Pharmacol Sci 29:576–581
Payne DA et al (2007) Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat Rev Drug Disc 6:29–40
Zhang Y (2005) The magic bullets and tuberculosis drug targets. Annu Rev Pharmacol Toxicol 45:529–564
Carpy AJ, Marchand-Geneste N (2006) Structural e-bioinformatics and drug design. SAR QSAR Environ Res 17(1):1–10
Ertl P, Jelfs S (2007) Designing drugs on the internet? Free web tools and services supporting medicinal chemistry. Curr Top Med Chem 7(15):1491–1501
Munos B (2006) Can open-source R&D reinvigorate drug research? Nat Rev Drug Discov 5(9):723–729
Tralau-Stewart CJ et al (2009) Drug discovery: new models for industry-academic partnerships. Drug Discov Today 14(1–2):95–101
Williams AJ (2008) Internet-based tools for communication and collaboration in chemistry. Drug Discov Today 13(11–12):502–506
Williams AJ (2008) A perspective of publicly accessible/open-access chemistry databases. Drug Discov Today 13(11–12):495–501
Ekins S et al (2008) Molecular characterization of CYP2B6 substrates. Curr Drug Metab 9(5):363–373
Ekins S et al (2008) Computational discovery of novel low micromolar human pregnane X receptor antagonists. Mol Pharmacol 74:662–672
Nwaka S, Ridley RG (2003) Virtual drug discovery and development for neglected diseases through public-private partnerships. Nat Rev Drug Discov 2(11):919–928
Hohman M et al (2009) Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. Drug Disc Today 14:261–270
Gamo F-J et al (2010) Thousands of chemical starting points for antimalarial lead identification. Nature 465:305–310
Ekins S, Williams AJ (2010) Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs. Med Chem Comm 1:325–330
Ekins S, Williams AJ (2010) When pharmaceutical companies publish large datasets: an abundance of riches or fool’s gold? Drug Disc Today 15:812–815
Ekins S et al (2010) Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. Mol Biosyst 6:2316–2324
Metz JT, Huth JR, Hajduk PJ (2007) Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J Comput Aided Mol Des 21(1–3):139–144
Huth JR et al (2005) ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens. J Am Chem Soc 127(1):217–224
Lipinski CA et al (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Del Rev 23:3–25
Oprea TI (2002) Current trends in lead discovery: are we looking for the appropriate properties? J Comput Aided Mol Des 16:325–334
Oprea TI et al (2001) Is there a difference between leads and drugs? A historical perspective. J Chem Inf Comput Sci 41:1308–1315
Rosen J et al (2009) Novel chemical space exploration via natural products. J Med Chem 52:1953–1962
Ekins S et al (2010) A collaborative database and computational models for tuberculosis drug discovery. Mol Biosyst 6:840–851
Guiguemde WA et al (2010) Chemical genetics of Plasmodium falciparum. Nature 465(7296):311–315
Gagaring, K., et al. Novartis-GNF malaria box. [cited]; Available from: ChEMBL-NTD (www.ebi.ac.uk/chemblntd)
Maddry JA et al (2009) Antituberculosis activity of the molecular libraries screening center network library. Tuberculosis (Edinb) 89:354–363
Ananthan S et al (2009) High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv. Tuberculosis (Edinb) 89:334–353
Ekins S, Freundlich JS (2011) Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets. Pharm Res 28:1859–1869
Ekins S, Williams AJ (2010) Reaching out to collaborators: crowdsourcing for pharmaceutical research. Pharm Res 27(3):393–395
Williams AJ et al (2009) Free online resources enabling crowdsourced drug discovery. Drug Discov World 10:33–38
Louise-May S, Bunin B, Ekins S (2009) Towards integrated web-based tools in drug discovery. Touch Brief Drug Discov 6:17–21
Bingham A, Ekins S (2009) Competitive collaboration in the pharmaceutical and biotechnology industry. Drug Disc Today 14:1079–1081
Acknowledgments
The authors gratefully acknowledge our colleagues and the many researchers in the CDD community who have collaborated with us and each other.
The CDD TB database is funded by the Bill and Melinda Gates Foundation (Grant#49852 “Collaborative drug discovery for TB through a novel database of SAR data optimized to promote data archiving and sharing”).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Ekins, S., Bunin, B.A. (2013). The Collaborative Drug Discovery (CDD) Database. In: Kortagere, S. (eds) In Silico Models for Drug Discovery. Methods in Molecular Biology, vol 993. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-342-8_10
Download citation
DOI: https://doi.org/10.1007/978-1-62703-342-8_10
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-341-1
Online ISBN: 978-1-62703-342-8
eBook Packages: Springer Protocols