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.

Unlike traditional methods that simulate physical interactions like a key in a lock, ProPhet's AI learns the fundamental patterns governing why certain molecules and proteins interact. This allows for prediction without needing slow, expensive, and often impossible physical or computational simulations.

The next major AI breakthrough will come from applying generative models to complex systems beyond human language, such as biology. By treating biological processes as a unique "language," AI could discover novel therapeutics or research paths, leading to a "Move 37" moment in science.

The next leap in biotech moves beyond applying AI to existing data. CZI pioneers a model where 'frontier biology' and 'frontier AI' are developed in tandem. Experiments are now designed specifically to generate novel data that will ground and improve future AI models, creating a virtuous feedback loop.

While AI promises to design therapeutics computationally, it doesn't eliminate the need for physical lab work. Even if future models require no training data, their predicted outputs must be experimentally validated. This ensures a continuous, inescapable cycle where high-throughput data generation remains critical for progress.

While AI can accelerate the ideation phase of drug discovery, the primary bottleneck remains the slow, expensive, and human-dependent clinical trial process. We are already "drowning in good ideas," so generating more with AI doesn't solve the fundamental constraint of testing them.

Profluent CEO Ali Madani frames the history of medicine (like penicillin) as one of random discovery—finding useful molecules in nature. His company uses AI language models to move beyond this "caveman-like" approach. By designing novel proteins from scratch, they are shifting the paradigm from finding a needle in a haystack to engineering the exact needle required.

Bob Nelsen believes the industry overestimates AI's short-term impact and underestimates its long-term potential. He predicts that once a critical data threshold is met, AI models won't just accelerate drug discovery but will fundamentally invent new biology, creating a sudden, paradigm-shifting moment.

According to Immunocore's CEO, the biggest imminent shift in drug development is AI. The critical need is not for AI to replace scientists, but for a new breed of professionals fluent in both their scientific domain and artificial intelligence. Those who fail to adapt will be left behind.