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The model for drug discovery and development is failing patients. It is expensive and high risk, with long research and development cycles. This has a societal cost, with 9,000 diseases being untreated - in addition to the disappointing reality that the top ten best-selling drugs only are effective in 30-50% of patients. Tackling this challenge is very complex. While many companies focus on one component of the drug discovery process, BenevolentAI chooses to apply data and machine-learning driven methods across drug discovery, from the processing of scientific literature, to knowledge completion, to precision medicine, to chemistry optimization, each leveraging domain expert knowledge and state-of-the-art research.

In this talk, we will discuss the peculiarities of machine learning for the drug discovery domain. In this field, there exist many unique challenges, including tradeoffs between novelty and accuracy; questions of quality and reliability, both in extracted data and in the underlying ground-truth; how best to learn from small volumes of data; and methods to best combine human experts and ML methods. As we discuss the tools and methods that BenevolentAI has developed, we will explore these themes and walk through approaches.

Finally, to give a real example of how we apply machine learning and AI in our day-to-day work, we will showcase the application of our technology to repurpose existing drugs, using our tools and internal clinical experts, as a potential treatment for COVID-19. Baracitinib, the top drug we identified is currently being investigated in a Phase 3 clinical trial.

Presenters: Daniel Neil, VP Artificial Intelligence, Sia Togia, AI Lead for Knowledge (NLP & Knowledge Graph), Olly Oechsle, Lead Application Engineer and Aylin Cakiroglu, Senior AI Scientist at BenevolentAI ‍

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