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Success in Global Challenge To Find Novel Antimalarial Compounds
Product News

Success in Global Challenge To Find Novel Antimalarial Compounds

Success in Global Challenge To Find Novel Antimalarial Compounds
Product News

Success in Global Challenge To Find Novel Antimalarial Compounds


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Intellegens, an artificial intelligence (AI) start-up, and, Optibrium™, 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 deep neural network algorithm, 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 recognized 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, synthesize 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 modeling platform Alchemite, applied by Optibrium, has been commended for its ability to predict active compounds with a novel mechanism of action. Alchemite can 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.


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