🏆 The Nobel Prize in Chemistry this year went to the creators of #AlphaFold: John Jumper, Demis Hassabis, and David Baker. Check out this article for examples of how AI-powered protein design is changing #biotech.
When Schrödinger launched more than 30 years ago, computational biology was a nascent technology. Schrödinger, a pioneer in the field, relied heavily on the Protein Data Bank (PDB), a collection of solved protein structures that got its start in the 1970s. “Getting a structure in the lab used to be—and still is—difficult,” says Karen Akinsanya, Schrödinger’s president of therapeutics R&D. Back then, a single protein structure would potentially comprise a person’s entire PhD or postdoctoral research, taking “years of work,” she says. The entire field of structure-based drug design took a giant leap forward with the advent of #AlphaFold, the protein prediction software that launched in 2020 and this week became a Nobel Prize–winning technology. AlphaFold, which is owned by Google’s DeepMind, built on the PDB and other protein sequence databases, has used a neural network to predict the structures of now millions of proteins. Schrödinger and its ilk use AlphaFold to help dream up drug candidates with a higher degree of specificity than what was previously possible. Akinsanya says her team is using a better understanding of how proteins fold to design, for instance, small molecules that change how those proteins interact with each other. Recently, a Schrödinger team used structure predictions of a protein encoded by the human ether-à-go-go–related—or hERG—gene, to see how 14 compounds would bind with it, then design drugs that would avoid the protein since inhibiting it can elicit severe cardiotoxic side effects (Cell 2024, DOI: 10.1016/j.cell.2023.12.034). “It’s accelerating the work of humans. There’s no doubt about that,” Akinsanya says of AlphaFold. “We are in the century of the protein.” It’s also the century of the algorithm. More in C&EN: https://lnkd.in/eBXX_D9U
