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Intellegens and Optibrium celebrate success in global challenge to find novel antimalarial compounds

publication date: Dec 16, 2019
 | 
author/source: Optibrium

intellegens-and-optibrium-celebrate-success-global


Team deploy Alchemite™ deep learning platform to predict active compounds for malarial drug discovery

Intellegens, an artificial intelligence (AI) start-up, and, Optibrium™, leading providers of software and services for drug discovery, have announced joint success in the Open Source Malaria (OSM) global initiative aimed at identifying the best predictive models for antimalarial compounds. Together, the companies developed one of the top models, deploying a cutting-edge deep neural network algorithm [1], Alchemite™, to accurately predict active compounds with novel mechanisms of actions that could be critical to future malaria control and elimination. As one of four prizewinning models selected, the project will now progress through the next phase of the initiative that includes the proposal of new compounds that are predicted to be active, for synthesis and testing against the malaria parasite.

Founded in 2012 by Professor Matthew Todd, Chair of Drug Discovery at University College London, the OSM consortium aims to find a new medicine for the treatment of malaria, which is formally recognised as a neglected tropical disease by the World Health Organisation. Over the past six years, OSM has brought together an international team of researchers who design, synthesise and test new antimalarial candidates that they hope will demonstrate potent activity against Plasmodium falciparum, the deadliest species of the malaria-causing parasite, in vitro and in vivo.

In the latest phase of the initiative, Intellegens’ predictive modelling platform Alchemite, applied by Optibrium, has been commended for its ability to predict active compounds with a novel mechanism of action. Alchemite has shown to significantly improve the accuracy of the predictions and outperform conventional quantitative structure-activity relationship (QSAR) models and other well-known approaches, thereby reducing research and development costs associated with the unneeded synthesis of inactive compounds.

Dr Tom Whitehead, Machine Learning Scientist at Intellegens, commented: “Alchemite demonstrates real-world applicability and has the potential to provide accurate predictions for problems in drug discovery, such as finding active compounds that can counteract tropical diseases like Malaria.”

Dr Benedict Irwin, Senior Scientist at Optibrium, said: “In order to combat the increasing incidences of resistance to antimalarial medication, it is essential to discover new compounds with novel mechanisms of action. We have previously seen that the Alchemite method can add significant predictive value across a range of projects and data sets both large and small. The Open Source Malaria data set was a new challenge and we are thrilled that our partnership with Intellegens has been recognised by the consortium and we look forward to progressing to the next phase of the initiative.”

Professor Matthew Todd, Founder of the OSM consortium, added: “It's frequently the case in infectious disease drug discovery that we're working without knowledge of the mechanism of action. This so-called phenotypic drug discovery can make it a challenge to see the patterns in the data in order to predict what to make next. I hope that new developments in AI and machine learning (ML) can help us to make our research more predictive and hence more efficient. The recent competition in Open Source Malaria, where teams openly contributed models to improve a promising series of antimalarials, suggests that new AI/ML technologies have enormous promise. Congratulations to the Optibrium/Intellegens team for contributing one of the best models, using Alchemite. We're excited by the new molecules that were suggested because they are not ones that we would necessarily have thought of ourselves. We're now making them in the lab.”

[1] Whitehead et al. J. Chem. Inf. Comput. Model. (2019) 59(3) pp. 1197-1204

 

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