Back in January 2019 AI3SD announced their first funding call. We funded 3 pilot projects, and one of the successful applicants of this funding call was Professor Mat Todd from…
This one-day meeting concerns the application of machine learning/artificial intelligence (ML/AI) approaches to the discovery of new drug leads. Specifically the meeting is about cases where the biological target is not clearly established - so-called phenotypic drug discovery. The meeting centers on a real example - a competition run by Open Source Malaria (OSM), funded by a grant from the EPSRC/AI3SD+ Network. Data on active and inactive compounds in one OSM antimalarial series were published online, and anyone was able to submit a model able to predict the actives. The models were judged against a dataset that was kept private, and the winners were asked to use their models to predict novel molecules. These are currently being made in the lab and biologically evaluated, and the results will be reported at the meeting, providing a real-world test, and a complete case study, of the capabilities of ML/AI approaches to accelerate modern drug discovery. We will hear from some of the eleven competition entrants about how their models were constructed, and will have other presentations on related developments. We hope during this meeting to establish which approaches worked well, which did not, and why. All those interested in the application of ML/AI methods to drug discovery are encouraged to attend. The meeting is free, but there will be a cap on numbers, meaning first come first served, meaning registration is essential. Lunch will be provided as part of this event. GitHub Repository: https://github.com/OpenSourceMalaria/Series4_PredictiveModel