Case Study with Optibrium and Open Source Malaria
“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.”
– Dr Matthew Todd, Professor of Drug Discovery at UCL School of Pharmacy and founder of the OSM.
- Alchemite™ machine learning was able to predict a new antimalarial compound, which progressed to synthesis and testing.
- The study succeeded despite the sparsity of available experimental bioactivity data.
- Alchemite™ outperformed four alternative approaches assessed by the consortium.
Malaria is a parasitic infection transmitted by mosquitoes in tropical countries. A single bite from a mosquito carrying the disease is enough to become infected and, if not diagnosed and treated promptly, it can be fatal. There are approximately half a million deaths every year worldwide.
The goal of this project was to identify new antimalarial compounds using a novel mechanism of action. Over the past six years, the Open Source Malaria (OSM) consortium has brought together an international team of researchers who design, synthesise, and test new antimalarial candidates with the hope that they will demonstrate potent activity against Plasmodium falciparum, the deadliest species of the malaria-causing parasite. However, the available experimental bioactivity data is sparse; for all of the different types of experiments that are available, only a very small proportion of the compounds have actually been measured.
Intellegens participated with our partners, Optibrium, leading providers of drug discovery software, in a global challenge organised by the Open Source Malaria consortium to design new antimalarial compounds with a novel mechanism of action, overcoming the challenge of sparse experimental data. Intellegens and Optibrium achieved joint success at designing new antimalarial compounds.