Keynote Presentation: Dr. Jason Rolfe, Co-Founder and CTO, Variational AI

This keynote is presented as part of the SBME Modern AI Summer Intensive, a five-day workshop exploring the foundations, methods, and real-world applications of modern deep learning and artificial intelligence. All are welcome to join the keynote presentation, registration is not required.

Join Jason Rolfe, Co-Founder and CTO of Variational AI, for a keynote on the application of generative AI and machine learning in drug discovery. Drawing on nearly two decades of experience in machine learning research, Jason will share insights into how foundation models are transforming the design and optimization of novel therapeutic molecules.

Dr. Jason Rolfe’s Biography:

Jason Rolfe is the Co-Founder and Chief Technology Officer of Variational AI, a Vancouver-based biotechnology company pioneering the use of generative artificial intelligence in drug discovery. With nearly two decades of experience in machine learning and generative modeling, Rolfe has been instrumental in developing Enki™, the biopharma industry’s first commercially available foundation model for small molecules.

Before founding Variational AI in 2019, Rolfe held the position of Principal Machine Learning Researcher at D-Wave Systems Inc., where he developed novel generative ML algorithms, and worked on applications to areas including epidemiology, transcriptomics, and industrial process control. His academic background includes a Ph.D. from Caltech and a postdoc with Yann LeCun at NYU.

At Variational AI, Rolfe leads the development of Enki™, a generative AI platform designed to create novel, drug-like small molecules optimized for multiple pharmacological properties. Unlike traditional AI models adapted from domains like image or text processing, Enki™ is purpose-built to handle the complex, graph-based structures of molecules, enabling the efficient exploration of vast chemical spaces. This approach allows for the rapid identification of potent, selective, and novel compounds, significantly reducing the time and cost associated with early-stage drug discovery.

Rolfe is also an active contributor to the scientific community, sharing insights through Variational AI’s Substack publication, “Musings on AI for Drug Discovery.” In his writings, he explores the challenges of applying machine learning to drug discovery, particularly the limitations of traditional quantitative structure-activity relationship (QSAR) models and the need for algorithms that can generalize beyond existing chemical data.

Under Rolfe’s technical leadership, Variational AI has achieved significant milestones, including collaborations with biopharmaceutical partners and the development of AI-generated molecules that have demonstrated superior performance compared to traditional high-throughput screening methods. His work continues to push the boundaries of what’s possible at the intersection of artificial intelligence and drug discovery.