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.

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 ...

Non-canonical amino acids can expand the scope of proteins available for therapeutics and machine learning platforms can ...

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 ...