Experimental validation of predictive models in a series of novel antimalarials

Author

  • E. G. Tse et al.

This article outlines an open drug discovery competition, help by the Open Source Malaria consortium. Optibrium entered in collaboration with Intellegens. We developed potential anti-malarials by combining our Cerella™ technology and StarDrop™ drug discovery software. We achieved second place with a compound flagged by Cerella. An unusual compound structure, our entry would not have been considered by human scientists alone, showing the usefulness of our machine learning methods.

Graphical abstract showing process of the open drug discovery competition, in which teams used AI and ML methods to predict compounds with good antimalarial activity, which were then synthesised and tested.

Citation details

E.G. Tse, L. Aithani, M. Anderson, J. Cardoso-Silva, G. Cincilla, G. J. Conduit, M. Galushka, D. Guan, I. Hallyburton, B. W. J. Irwin, K. Kirk, A. M. Lehane, J. C. R. Lindblom, R. Lui, S. Matthews, J. McCulloch, A. Motion, H. L. Ng, M. Öeren, M. N. Robertson, V. Spadavecchio, V. A. Tatsis, W. P. van Hoorn, A.D. Wade, T. M. Whitehead, P. Willis, and M. H. Todd et al., J. Med. Chem. 2021, 64, 22, 16450–16463
DOI: 10.1021/acs.jmedchem.1c00313

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Download the full article describing the outcomes of the open drug discovery competition from the journal webpage via the button below. Alternatively, find out more about AI-guided design of antimalarials by watching our webinar. Ben Irwin and Matthew Segall (Optibrium) and Professor Matthew Todd (University College London) describe how Optibrium’s team deployed cutting-edge AI technologies and used predictive models to design active compounds against a novel target in Plasmodium falciparum.

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