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Historically, biological discovery relied on harvesting naturally occurring proteins from bacteria or organisms like CRISPR and PCR. By training large language models on sequential protein data instead of natural text, Retro Biosciences and OpenAI demonstrated that bio-native models can generate completely new, functional human proteins at will. This transition shifts protein engineering from scrounging natural biology to an exponential, de novo generative paradigm.
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Retro Biosciences engineered proteins that reprogrammed aged cells 50 times more efficiently than standard methods. They achieved this in months by training a protein language model with OpenAI, compressing a process that took academics a decade and showcasing a dramatic acceleration in engineering timelines.
Diffusion models were a breakthrough for protein generation because they reframe the problem. Instead of a one-shot generation, they learn to make many small, iterative refinements ("make it slightly better"). This "time to think" approach proved more effective for complex biological structures than previous methods like VAEs.
Training a language model to predict the next amino acid in a sequence forces it to learn the protein's 3D structure. To make accurate predictions, the model must understand an amino acid's physical microenvironment, effectively deriving 3D spatial relationships from 1D sequence data alone. This demonstrates emergent capabilities of LLMs in biology.
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.
Novonesis has shifted enzyme discovery from the lab to computers. Using AI tools like AlphaFold, they predict protein structures and identify new enzyme families based on structural motifs rather than sequence similarity. This allows them to find novel functionalities much faster 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.
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.
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.
Resvita Bio's approach isn't about creating proteins from scratch. Instead, they use machine learning to 'read the book of life comprehensively,' analyzing how different organisms have evolved to solve the same biological problem. This allows them to synthesize nature's best solutions into an ideal therapeutic protein.
Generative AI alone designs proteins that look correct on paper but often fail in the lab. DenovAI adds a physics layer to simulate molecular dynamics—the "jiggling and wiggling"—which weeds out false positives by modeling how proteins actually interact in the real world.