A whole new category of generative AI is emerging as LLMs learn the language of life.
10^1,300—an unfathomably large number, many times greater than the number of atoms in the universe.
We will be able to design new protein therapeutics to address the full gamut of human illness—from cancer to autoimmune diseases, from diabetes to neurodegenerative disorders. Looking beyond medicine, we will be able to create new classes of proteins with transformative applications in agriculture, industrials, materials science, environmental remediation and beyond.
Why are language models such a promising path forward compared to other computational approaches to protein design? One key reason: scaling.: the fact that almost unbelievable improvements in performance result from continued increases in LLM parameter count, training data and compute. Protein sequence data can be tokenized and for all intents and purposes treated as textual data; after all, it consists of linear strings of amino acids in a certain order, like words in a sentence. Large language models can be trained solely on protein sequences to develop a nuanced understanding of protein structure and biology.
While he pioneered the use of LLMs for protein design, Madani is also clear-eyed about the fact that, by themselves, off-the-shelf language models trained on raw protein sequences are not the most powerful way to tackle this challenge. Incorporating structural and functional data is essential.
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