TY - JOUR
T1 - Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases
AU - Williams, Kevin Stewart
AU - Bilsland, Elizabeth
AU - Sparkes, Andrew Charles
AU - Aubrey, Wayne
AU - Young, Michael
AU - Soldatova, Larisa Nikolaevna
AU - de Grave, Kurt
AU - Ramon, Jan
AU - de Clare, Michaela
AU - Sirawaraporn, Worachart
AU - Oliver, Stephen G.
AU - King, Ross
N1 - UK Biotechnology and Biological Sciences Research Council (BB/F008228/1;
European Commission under the FP7 Collaborative Programme, UNICELLSYS; KU Leuven (GOA/08/008); ERC (240186)
PY - 2015/3/3
Y1 - 2015/3/3
N2 - 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
AB - 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
KW - drug design
KW - artificial intelligence
KW - quantitative structure activity relationship
UR - http://hdl.handle.net/2160/41956
U2 - 10.1098/rsif.2014.1289
DO - 10.1098/rsif.2014.1289
M3 - Article
SN - 1742-5689
VL - 12
JO - Interface
JF - Interface
IS - 104
M1 - 20141289
ER -
