title: Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases.
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.
authors: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.
authors: Kevin
authors: K
authors: Williams
authors: 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.
authors: Elizabeth
authors: E
authors: Bilsland
authors: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.
authors: Andrew
authors: A
authors: Sparkes
authors: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.
authors: Wayne
authors: W
authors: Aubrey
authors: Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DD, UK.
authors: Michael
authors: M
authors: Young
authors: Department of Computer Science, Brunel University, London UB8 3PH, UK.
authors: Larisa N
authors: LN
authors: Soldatova
authors: Department of Computer Science, KU Leuven, 3001 Heverlee, Belgium.
authors: Kurt
authors: K
authors: De Grave
authors: Department of Computer Science, KU Leuven, 3001 Heverlee, Belgium.
authors: Jan
authors: J
authors: Ramon
authors: Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK.
authors: Michaela
authors: M
authors: de Clare
authors: Department of Biochemistry, Mahidol University, Thailand.
authors: Worachart
authors: W
authors: Sirawaraporn
authors: Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK.
authors: Stephen G
authors: SG
authors: Oliver
authors: Manchester Institute of Biotechnology and School of Computer Science, University of Manchester, Manchester M1 7DN, UK ross.king@manchester.ac.uk.
authors: Ross D
authors: RD
authors: King
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use; D001331:Automation; D015195:Drug Design; D004353:Drug Evaluation, Preclinical; D058492:Drug Repositioning; D006801:Humans; D016780:Malaria, Vivax / Q000188:drug therapy; D015233:Models, Statistical; D010966:Plasmodium vivax / Q000187:drug effects; D021281:Quantitative Structure-Activity Relationship; D035583:Rare Diseases / Q000188:drug therapy*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012984:Software; D013678:Technology, Pharmaceutical / Q000639:trends*; D014330:Tropical Medicine
chemical_list: D000970:Antineoplastic Agents
keywords: artificial intelligence; drug design; quantitative structure activity relationship
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