Moving against malaria

Optibrium and Intellegens announce in vitro validation of in silico-generated compound for the Open Source Malaria initiative

Register for free to listen to this article
Listen with Speechify
CAMBRIDGE, U.K.—Optibrium Ltd., a provider of software and services for drug discovery, and Intellegens Limited, a spin-out from the University of Cambridge with a unique Artificial Intelligence (AI) toolset, announced today that the companies have reached another 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 2, predictive models from Phase 1 were combined with generative methods to design novel compounds. The compounds were then validated by testing their activity against the target. Out of four compounds proposed in this phase, only the Optibrium/Intellegens team’s entry demonstrated potency against the target. According to Optibrium, this indicates that the combination of StarDrop, the company’s computational platform for small molecule design and optimization, and their AI-powered Augmented Chemistry platform is a powerful one.
“Our latest work with the Open Source Malaria consortium is a testament to the power of Optibrium’s software. It demonstrates, 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,” said Dr. Benedict Irwin, senior scientist at Optibrium.
In the latest phase of the OSM project, Optibrium and Intellegens 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. The predictive models were applied to guide the design efforts in Phase 2. With the combination of StarDrop and Augmented Chemistry technologies, the team designed the sole compound for which activity was confirmed using in vitro tests. The measured activity was in strong agreement with the predicted values.
“This result is a powerful validation of the benefit advanced deep learning methods such as Alchemite can bring to chemistry design and optimization problems,” stated Dr. Tom Whitehead, head of Machine Learning at Intellegens. “We are looking forward to continuing to support OSM in their pursuit of new treatments for malaria.”
The OSM consortium was founded in 2012 by Professor Matthew Todd, chair of Drug Discovery at University College London. The project aims to find new medicines for the treatment of malaria, which is recognized by the World Health Organization as one of the world’s biggest killers. The latest results from the initiative can be found here.
“It’s great to see that the Optibrium/Intellegens’ strong modeling results from Phase 1 could be complemented with generative methods and held up in in vitro testing,” added Todd. “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.”

Subscribe to Newsletter
Subscribe to our eNewsletters

Stay connected with all of the latest from Drug Discovery News.

DDN Magazine May 2024

Latest Issue  

• Volume 20 • Issue 3 • May 2024

May 2024

May 2024 Issue