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 ...

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 ...

The integrated approach is designed to adapt to the evolving needs of new therapeutic modalities, delivering both speed and performance.

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.

Their overview highlights innovative methods based on B-factor analysis, ancestral sequence reconstruction (ASR), and machine learning (ML), providing tools to design enzymes that withstand high ...

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 ...

It has long been thought that protein function and stability are highly sensitive to changes in the composition of the internal structures, or protein cores. However, a large-scale experiment probing ...