Professor Collins' AI models, trained only to kill a specific pathogen, unexpectedly identified compounds that were narrow-spectrum—sparing beneficial gut bacteria. This suggests the AI is implicitly learning structural features correlated with pathogen-specificity, a highly desirable but difficult-to-design property.

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.

In high-stakes fields like pharma, AI's ability to generate more ideas (e.g., drug targets) is less valuable than its ability to aid in decision-making. Physical constraints on experimentation mean you can't test everything. The real need is for tools that help humans evaluate, prioritize, and gain conviction on a few key bets.

Professor Collins’ team successfully trained a model on just 2,500 compounds to find novel antibiotics, despite AI experts dismissing the dataset as insufficient. This highlights the power of cleverly applying specialized AI on modest datasets, challenging the dominant "big data" narrative.

While AI holds long-term promise for molecule discovery, its most significant near-term impact in biotech is operational. The key benefits today are faster clinical trial recruitment and more efficient regulatory submissions. The revolutionary science of AI-driven drug design is still in its earliest stages.

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.

The AI-discovered antibiotic Halicin showed no evolved resistance in E. coli after 30 days. This is likely because it hits multiple protein targets simultaneously, a complex property that AI is well-suited to identify and which makes it exponentially harder for bacteria to develop resistance.

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.

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.