Dr. James Field on Breaking the 'Cognition Barrier' in Antibody Discovery with AI and Robotics

In our conversation, we explore the challenges of traditional antibody discovery and how LabGenius navigates these challenges using mathematical models. We will discuss the concept of 'robot scientists,' understanding the unique challenges and rewards that this paradigm presents.

We delve into the recently released research demonstrating the company's ability to expedite the discovery of highly targeted molecules that have the potential to mitigate toxic side effects associated with existing immunotherapies. The conversation will also cover the importance of unique, high-quality datasets and their application in antibody discovery.

Lastly, we'll touch on the future of AI and robotics in healthcare, both in the context of LabGenius' goals and in the wider industry. We will examine Dr. Field's role in briefing policymakers on AI-enabled drug discovery, and explore how LabGenius plans to use its significant funding to further its mission.

Andrii: Could you expound on the "cognition barrier" you mention, and elaborate how LabGenius is utilizing mathematical models to understand molecular responses to diseases? What differentiates your approach from conventional methods? 

James: Humanity’s incredible success (at least reproductively) can largely be attributed to our capacity to hypothesise and invent. But even this superpower has limits. Concretely, hypothesis-driven innovation requires the inventor to understand the system that they’re working with at an appropriate level of abstraction. Now, here's the rub. There are many arenas in which hypothesis-driven innovation performs poorly because the human brain simply isn't wired to grapple with the complexity of the underlying system. 

For example, consider biological systems. Earth’s flora and fauna provide living proof of what can be built with biology, and at the same time highlight the sheer inadequacy of human-led hypothesis-driven innovation within this domain. This shouldn’t come as a surprise. After all, at no point in our evolutionary history has an intuition for manipulating organic matter at the nanoscale conferred a selective advantage!

To address some of humanity’s greatest challenges, it’s clear that we need to break through the ‘cognition barrier’ presented by hypothesis-driven innovation. In the absence of a suitable nootropic, this means that we must develop new forms of innovation that do not require a human to understand the underlying system. 

This concept is not new. For decades, scientists, engineers and technologists have dreamt of building ‘robot scientists’ capable of autonomously discovering new knowledge, technologies, and sophisticated real-world products. For protein engineers, that dream is now a real possibility.