StarDrop™ and Augmented Chemistry™ prove a powerful combination in AI-guided design (Alchemite™) and validation of novel antimalarial drug candidates. In silico designed compound demonstrates potency against pfATP4 protein target in malaria parasite.
CAMBRIDGE, UK, April 1, 2020– Intellegens, an Artificial Intelligence (AI) company with a unique deep learning toolset for sparse data and Optibrium™, leading providers of software and services for drug discovery, today announced they have reached a further significant milestone in their contribution to the Open Source Malaria (OSM) initiative. The team has successfully completed phase 2 of a global challenge aimed at developing and testing novel antimalarial compounds. During this phase, predictive models from phase 1 were combined with generative methods to design novel compounds. The compounds were subsequently validated by testing their activity against the target.
Out of four compounds proposed in this phase, only Optibrium/Intellegens’ entry demonstrated potency against the target indicating the powerful combination of StarDrop™, Optibrium’s computational platform for small molecule design and optimisation, with the AI-powered technologies (Alchemite™) of its Augmented Chemistry™ platform.
In the latest phase of the OSM project, the team deployed the in silico, generative chemistry capabilities of StarDrop™ to design new compounds predicted to be active against a putative target in Plasmodium falciparum, the deadliest species of malaria-causing parasites. In phase 1, Optibrium’s Augmented Chemistry™ technologies, which incorporate Intellegens’ Alchemite™  deep learning platform, were used to build accurate predictive models for activity against this target. These were applied to guide the design efforts in phase 2. Combining StarDrop™ and Augmented Chemistry™ technologies, the team designed the only compound,out of four submitted by different organisations, for which activity was confirmed using in vitro tests,and the measured activity was in strong agreement with the predicted values.
Founded in 2012 by Professor Matthew Todd, Chair of Drug Discovery at University College London, the OSM consortium aims to find new medicines for the treatment of malaria, which is recognised by the World Health Organisationas one of the world’s biggest killers. The latest results from the initiative can be found at:
Measured activity of 5 in silico generated compounds against pfATP4 assay
Benedict Irwin, Senior Scientist at Optibrium, said:
“Our latest work with the Open Source Malaria consortium is a testament to the power of Optibrium’s software. Itdemonstrates,in an open and transparent way,the impact this dynamic blend of computational chemistry and machine learning can have in supporting drug discovery scientists in tackling these serious diseases.”
Professor Matthew Todd, Founder of the OSM consortium, added:
“It’s great to see that the Optibrium/Intellegens’ strong modelling results from phase 1 could be complemented with generative methods and held up in in vitro testing.While the use of AI in drug discovery is still in its infancy and in many cases the potential of in silico designed compounds hasn’t yet been rigorously validated experimentally, this example can help pave the way and is a valuable contribution to our efforts. I hope to see more from the team in support of our quest to develop effective treatments for malaria.”
Dr Tom Whitehead, Head of Machine Learning at Intellegens, said:
“This result is a powerful validation of the benefit of advanced deep learning methods such as Alchemite™ can bring to chemistry design and optimisation problems. We are looking forward to continuing to support OSM in their pursuit of new treatments for malaria.”
For more information on how our deep learning technology aids drug discovery, go to https://intellegens.ai/drug-discovery/
For further information on StarDrop™or Augmented Chemistry™, please visit www.optibrium.com/stardrop/orwww.optibrium.com/augmentedchemistry, contact firstname.lastname@example.org or call +44 1223 815900.
 Whitehead et al.J. Chem. Inf. Comput. Model. (2019) 59(3) pp. 1197-1204