The discovery-based model of finding highly impactful single targets like HER2 or PD-1 is becoming unsustainable as the low-hanging fruit is picked. The field must shift toward an engineering-first approach, designing complex, multi-functional therapeutics to achieve specific clinical objectives, much like high-tech fields.

While biologics get much attention, a significant investment opportunity lies in next-generation small molecules like degraders and hetero-bifunctional molecules. These advanced chemistries allow companies to target known, de-risked biological pathways in novel ways, hitting previously 'undruggable' targets and creating powerful new drugs.

With directed evolution, scientists find a mutated enzyme that works without knowing why. Even with the "answer"—the exact genetic changes—the complexity of protein interactions makes it incredibly difficult to reverse-engineer the underlying mechanism. The solution often precedes the understanding.

For a modest 100-amino-acid protein, there are 10^130 possible sequences, while all life on Earth has only explored ~10^43. This vast, unexplored space means we can now design binders for "undruggable" targets that evolution never needed to create.

Frances Arnold, an engineer by training, reframed biological evolution as a powerful optimization algorithm. Instead of a purely biological concept, she saw it as a process for iterative design that could be harnessed in the lab to build new enzymes far more effectively than traditional methods.

Instead of screening billions of nature's existing proteins (a search problem), AI-powered de novo design creates entirely new proteins for specific functions from scratch. This moves the paradigm from hoping to find a match to intentionally engineering the desired molecule.

Generate Biomedicines' AI learns the fundamental rules of protein structure and function, much like a language's grammar. This allows it to design entirely new proteins by generating novel "sentences" (sequences) that are biologically coherent and functional, rather than just mimicking existing ones found in nature.

Beyond optimizing existing biological functions, Frances Arnold's lab uses directed evolution to create enzymes for entirely new chemical reactions, like forming carbon-silicon bonds. This demonstrates that life's chemical toolkit is a small subset of what's possible, opening up a vast "non-natural" chemical universe.

While AI can design countless new proteins, it is fundamentally limited by the 20 standard amino acids. The durable advantage for synthetic biology companies is the ability to build proteins with new-to-nature blocks, enabling chemical reactions and features that AI-designed proteins simply cannot achieve.