An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials
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Abstract
The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against Plasmodium falciparum in vitro and in vivo and that have been suggested to act through the inhibition of Pf ATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na + concentration. The structure of Pf ATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti- Pf ATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds. In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.
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- Authors
- Tse, E. G., Aithani, L., Anderson, M., Cardoso-Silva, J., Cincilla, G., Conduit, G. J., Galushka, M., Guan, D., Hallyburton, I., Irwin, B. W. J., Kirk, K., Lehane, A. M., Lindblom, J. C. R., Lui, R., Matthews, S., McCulloch, J., Motion, A., Ng, H. L., Öeren, M., Robertson, M. N., Spadavecchio, V., Tatsis, V. A., van Hoorn, W. P., Wade, A. D., Whitehead, T. M., Willis, P., & Todd, M. H.
- Affiliations
- UCL School of Pharmacy (GB)
- Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
- University of Dundee (GB)
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King’s College London, London WC2B 4BG, U.K.
- Parc Científic de Barcelona (ES)
- University of Cambridge (GB)
- Auromind Ltd, 126 Eglantine Avenue, Belfast BT9 6EU, U.K.
- The University of Sydney (AU)
- Australian National University (AU)
- Kellerberrin, 6 Wharf Rd, Balmain, Sydney, NSW 2041, Australia
- Kansas State University (US)
- Optibrium (United Kingdom) (GB)
- University of Strathclyde (GB)
- Interlinked Therapeutics LLC, Portland, Oregon 97214, United States
- Intellegens Ltd., Eagle Labs, Chesterton Road, Cambridge CB4 3AZ, U.K.
- Medicines for Malaria Venture (CH)
- Corresponding author
- Todd, Matthew H.
- ORCID
- Tse, Edwin G.
- Aithani, Laksh
- Cincilla, Giovanni
- Conduit, Gareth J.
- Guan, Davy
- Hallyburton, Irene
- Irwin, Benedict W. J.
- Kirk, Kiaran
- Lehane, Adele M.
- Lui, Raymond
- Matthews, Slade
- Motion, Alice
- Ng, Ho Leung
- Öeren, Mario
- Robertson, Murray N.
- Tatsis, Vasileios A.
- Wade, Alexander D.
- Whitehead, Thomas M.
- Willis, Paul
- Todd, Matthew H.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-11-08
- Volume
- 64
- Issue
- 22
- Pages
- 16450-16463
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W3109506729
- Cited by
- 18
- References
- 43
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 5 · 2022: 5
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Medicinal Chemistry (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion, cc-by)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (acceptedVersion, public-domain)
- ANU Open Research (Australian National University) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-nd)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/L015552/1, EP/L015552/2, EP/S000356/1)
- Royal Society
- Australian Research Council (LP150101226)
- Medicines for Malaria Venture (RD/11/0040)
- University College London
- National Science Foundation (1833181)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Malaria Research and Control
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- antimalarial drug discovery, Plasmodium falciparum, predictive modeling, machine learning, structure-activity relationship, experimental validation
- MeSH terms
- Calcium-Transporting ATPases, Antimalarials, Enzyme Inhibitors, Humans, Models, Biological, Plasmodium falciparum, Structure-Activity Relationship, Drug Discovery
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