The model for drug discovery and development is failing patients. It is expensive and high risk, with long research and development cycles. In this talk, we will discuss the peculiarities of machine learning across drug discovery, from the processing of scientific literature, to knowledge completion, target identification to precision medicine, to chemistry optimisation, each leveraging domain expert knowledge and state-of-the-art research and we will conclude our talk with a real example which will showcase the application of our technology and scientific expertise to repurpose existing drugs, as a potential treatment for COVID-19.
Why is drug discovery so complex? How can ML help to solve these scientific challenges? What does an ML applied drug discovery model look like? What skills are useful to the industry? If you are asking yourself some of these questions, then this meetup is for you.
Our AI Science Intern, Constantin Schneider, will present his paper on ‘Auxiliary task evaluations to learn meaningful representations from electronic health records', co-authored by BenevolentAI's Senior Machine Learning Researcher, Hamish Tomlinson, at the Learning Meaningful Representations of Life workshop.
Authors: Hamish Tomlinson, Constantin Schneider
Our team will be presenting their paper on 'Molecular representation learning with language models and domain-relevant auxiliary tasks' at the Machine Learning for Molecules Workshop.
Authors: Fabian et al. (2020)
While you're at NeurIPS, connect with our team virtually via the Hopin platform. Connect with our team at networking sessions to ask any questions you may have, and/or join our careers with impact meetup to learn more about what we do and how we work. The full programme will be coming soon. As we have limited space, we advise you to book your spot now.