Behind the Scenes at SXSW - Designing Better Medicines using AI and Big Data

By its own definition, South By Southwest dedicates itself to helping creatives achieve their goals and proves that the most unexpected discoveries happen when diverse topics and people come together. This is what brought the Benevolent team to Texas - to share our collective vision of using the power of technology to solve something that truly matters to the world. For us, this means tackling the most challenging and devastating diseases that currently have no cure, and we believe our efforts can and will change outcomes for patients.

In this video live from SXSW, our team of scientists and AI experts presented a ‘behind the scenes’ introduction to show how our latest advancements in machine learning and AI can lead to a better understanding of the underlying causes of diseases and redefine the way in which new medicines are discovered and brought to patients.

At SXSW, we used the example of Glioblastoma Multiforme, the very aggressive, currently incurable and most common form of brain cancer. We demonstrated something different and a little unconventional - the creative approach that our team of BenevolentAI scientists, AI experts, engineers and chemists take to come together to try and solve such a complicated disease.

AI helps us uncover relationships between diseases and symptoms, drugs and effects, the patients who respond to treatments and so much more. Relationships that would previously be hidden by the overwhelming volume of biomedical information and the inherent complexity of biology, diseases and the human body.

It’s too early to say if there will be an effective treatment for Glioblastoma any time soon. But every day, we improve our platform and increase the likelihood of finding a treatment. We hope to return to SXSW to demonstrate the progress we are making on this devastating disease.


00:00 - 08:29: Opening by Joanna Shields, BenevolentAI CEO

08:29 - 21:39: Building a disease specific knowledge base

24:16 - 33:12: Target ID | Literature knowledge inference

33:12 - 38:59: Patient Stratification | OMICS data

38:59 - 44:21: Next steps | From molecule design to medicine