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J R Soc Interface. 2015 Mar 6;12(104):20141289. doi: 10.1098/rsif.2014.1289.

Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases.

Author information

1
Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.
2
Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK Department of Structural and Functional Biology, UNICAMP, 13083-865, Campinas, São Paulo, Brazil.
3
Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DD, UK.
4
Department of Computer Science, Brunel University, London UB8 3PH, UK.
5
Department of Computer Science, KU Leuven, 3001 Heverlee, Belgium.
6
Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK.
7
Department of Biochemistry, Mahidol University, Thailand.
8
Manchester Institute of Biotechnology and School of Computer Science, University of Manchester, Manchester M1 7DN, UK ross.king@manchester.ac.uk.

Abstract

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

KEYWORDS:

artificial intelligence; drug design; quantitative structure activity relationship

PMID:
25652463
PMCID:
PMC4345494
DOI:
10.1098/rsif.2014.1289
[Indexed for MEDLINE]
Free PMC Article

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