How can artificial intelligence and deep-learning platforms be used to combat malaria?
Stephanie Sutton | | Interview
Despite all of effort and money that has gone into malaria research, increasing numbers of multi-drug resistant parasites are being reported, and the disease remains a significant global burden. Though several effective treatments have been developed, they fail to wipe out all of the parasites in hosts and are likely to encourage long-term resistance with selection pressure.
Optibrium – a provider of software and services for drug discovery; and Intellegens – a start-up focusing on AI, have collaborated on a predictive model that is able to identify antimalarial compounds. The new Alchemite-based platform was put to the test in the Open Source Malaria (OSM) global initiative, which invites scientists to share their research on malaria drugs through an online platform. The AI platform developed by the two companies uses novel mechanisms that could be critical to future malarial control and elimination. Here, we speak to Benedict Irwin, Senior Scientist at Optibrium, about the newly formed partnership and the role AI is playing in combating one of the world’s most deadly diseases.
How did you come to collaborate with Intellegens?
Both companies have strong links to the Theory of Condensed Matter (TCM) group in the Cavendish Laboratory at the University of Cambridge. Intellegens is a spin out company from TCM and our CEO, Matthew Segall, and I both did our PhDs in the group. It was during this time that we met Gareth Conduit, the CSO of Intellegens.
Intellegens’ initial research focused on material sciences; the team created Alchemite to help develop new super-alloys; in essence, the algorithm uses sparse and noisy data to make predictions. During discussions between Matthew and Gareth, it was quickly realized that the capabilities of the algorithm were ideally suited to applications in drug discovery; after all, our field produces a lot of sparse and noisy data, which Alchemite works well with! Furthermore, Optibrium’s extensive drug discovery domain knowledge had the potential to help Intellegens enter our sector through a collaborative partnership. The concept was further cemented with a grant from Innovate UK, which was used to fund a £1-million project called Deep ADMET (using deep-learning to predict the absorption, distribution, metabolism, excretion and toxicity of new drug candidates) in collaboration with the Medicines Discovery Catapult. We learned a lot from this project and other areas – and have since refined Alchemite.
How does Alchemite work?
Alchemite performs a process called “deep imputation,” which uses deep-learning algorithms to fill in the missing entries in a data set with predictions. The predictions can be based on structural descriptors, as well as any existing and/or measured datapoints themselves, even if most of the potential data have not yet been measured. Not only can the algorithm learn from structure-activity relationships, as used by conventional quantitative structure-activity relationship (QSAR) models, but also assay–assay correlations, which provide much more information to make more accurate predictions.
One of the other strengths of the method is that it provides an error-bar on every prediction, so we know the confidence in each one. It is impossible to get a perfect model of biological data, especially if there is a lot of noise in the original data, but with Alchemite we can focus on the accurate predictions hidden underneath the noise, regardless of the overall quality of the data. It is this feature that helped generate reliable predictions for active compounds for the OSM project.
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