Dr. Abelson credits his undergraduate training in experimental psychology as being invaluable for his career in clinical research. It taught him the fundamentals of writing a protocol, analyzing data, and identifying flaws in a study—skills he directly applied to drug development decades later.

AI modeling transforms drug development from a numbers game of screening millions of compounds to an engineering discipline. Researchers can model molecular systems upfront, understand key parameters, and design solutions for a specific problem, turning a costly screening process into a rapid, targeted design cycle.

Instead of testing a single drug candidate in cheap models before moving to expensive ones, Gordian's parallel testing platform makes it cost-effective to use clinically relevant large animals, like horses, at the very beginning of the discovery process. This flips the traditional R&D funnel on its head.

Dr. Abelson’s career spans the transformation of clinical research from an endeavor led by a single physician-scientist to a complex industry with specialized companies for statistics, patient recruitment, and regulatory affairs. This specialization has enabled the current rapid pace of drug development.

Unlike using genetically identical mice, Gordian tests therapies in large, genetically varied animals. This variation mimics human patient diversity, helping identify drugs that are effective across different biological profiles and addressing patient heterogeneity, a primary cause of clinical trial failure.

The NIH will no longer award funding to new grant proposals that rely exclusively on animal models. This policy forces a shift towards New Approach Methodologies (NAMs), such as organoids and organ-on-chips, serving as a major catalyst for innovation and adoption in the preclinical testing space.

CZI's virtual cell models act as a computational "model organism," enabling scientists to run high-risk experiments in silico. This approach dramatically lowers the cost and time required to test novel ideas, encouraging more ambitious research that might otherwise be prohibitive.

The most impactful medical advances come from 'clinical scientists' who both see patients and work in the lab. This dual perspective provides a deep understanding of disease mechanisms and how to translate research into treatments, a model that Dr. Abelson believes is now under threat due to economic pressures.

Following the success of AlphaFold in predicting protein structures, Demis Hassabis says DeepMind's next grand challenge is creating a full AI simulation of a working cell. This 'virtual cell' would allow researchers to test hypotheses about drugs and diseases millions of times faster than in a physical lab.