The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 possible variants-more combinations than atoms in the observable universe.

In this video, Arc Institute Postdoctoral Fellow Vincent Tran walks through MULTI-evolve, an AI-guided framework that compresses protein engineering from months of iterative experimentation into weeks ...

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments. In a study published in the journal Nature ...

CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein engineering for cancer treatment. Operating significantly faster than traditional all ...

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational model that could expedite the use of nanomaterials in biomedical applications.

AI language models, used to generate human-like text to power chatbots and create content, are also revolutionizing biology ...

Scientists have used deep learning to design new proteins that bind to complexes involving other small molecules like hormones or drugs, opening up a world of possibilities in the computational design ...

Marc Zimmer does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their ...

A generalizable ML framework predicts protein interactions with ligand-stabilized gold nanoclusters, supporting faster design of bioimaging, sensing and drug delivery materials. (Nanowerk News) The ...