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

Kevin Stewart Williams, Elizabeth Bilsland, Andrew Charles Sparkes, Wayne Aubrey, Michael Young, Larisa Nikolaevna Soldatova, Kurt de Grave, Jan Ramon, Michaela de Clare, Worachart Sirawaraporn, Stephen G. Oliver, Ross King

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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
Original languageEnglish
Article number20141289
JournalInterface
Volume12
Issue number104
Early online date04 Feb 2015
DOIs
Publication statusPublished - 03 Mar 2015

Keywords

  • drug design
  • artificial intelligence
  • quantitative structure activity relationship

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