Accelerating the Next Generation of Therapies with the dAIsY™ AI Platform

Jennifer Towne has always been driven by her instincts to question and explore. This mindset has shaped her career across molecular genetics, biochemistry, and microbiology, spanning more than two decades in immunology research. As she advanced numerous drug candidates to the clinic, led global R&D teams and ultimately became chief scientific officer at Vir Biotechnology, her motivation has remained constant.

“My passion is digging into really great science to be able to bring forward therapies that benefit and transform the way we treat patients,” she says.

At Vir Biotechnology, that passion is accelerated by dAIsY™, short for data, AI, structure and antibody, a proprietary in-house AI platform that blends human insight with rapid, multiparameter computational design of monoclonal antibodies and T-cell engagers.

Biology first, AI embedded

The company’s expertise in immunology and antibody development forms the foundation for its approach to drug discovery.

“We apply dAIsY™ to every discovery project, and improvements are always possible,” Towne says. “Generally, we see substantial time and cost savings tied to our discovery phase and generate higher quality molecules.”

Rather than operating as a standalone tool, dAIsY™ is built into the core of the research process, supporting design, testing and refinement in a continuous workflow.

A continuous loop between computation and the lab

What gives dAIsY™ its power is not prediction alone, but how it continuously learns and improves. Scientists use dAIsY™ to optimize molecules, test them in the lab and feed those results back into the system, so each round of work builds on what came before.

“What’s important is that AI isn’t a separate function here,” Towne says. “It’s seamlessly embedded into the core of how we work, instead of a separate part of the drug discovery process.”

From sequential to simultaneous design

dAIsY™ also enables simultaneous optimization across key properties improving functionality such as potency and stability.

“We can refine multiple parameters of the molecule simultaneously without compromising already desirable properties,” Towne says.

The platform can also be applied to any protein and molecule type, from monoclonal antibodies to T-cell engagers.

Identifying ideal treatment candidates

Drug development often uncovers critical limitations late in the process, after years of work. By shifting that learning earlier and enabling broader exploration upfront, dAIsY™ helps identify more promising solutions from the start.

For Towne, dAIsY™ represents more than a technology, it reflects a new way of working.

“The goal isn’t just speed,” Towne says. “It’s about integrating everything into one continuous process to learn and improve, ultimately developing treatment candidates with optimal properties that can truly improve outcomes for patients who are waiting.”