The current model for drug discovery and development is failing patients: the top ten selling drugs are only effective on less than 50% of the population and there are still 9000+ diseases with no effective treatments. There is an urgent need to innovate and search for better solutions for drug development. Machine learning represents a great opportunity to do just that. In this presentation, Alix Lacoste will present a case study for tensor factorisation for drug target identification, and discuss how to evaluate models for drug discovery applications and the need for diversity of biomedical datasets.
Alix has significant experience using data science and machine learning to advance biomedical discoveries. She holds a PhD in Molecular and Cellular Biology from Harvard University. Previously at IBM Watson Health, Alix led computational research projects in target identification and drug repurposing, most notably for Parkinson’s disease and amyotrophic lateral sclerosis, in collaboration with academic and pharma partners. At BenevolentAI, Alix connects AI and Drug Discovery groups to continuously improve the hypothesis generation pipeline.
