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

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 a new study published in Nature titled, “Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning,” researchers from Massachusetts General Hospital (MGH) and Harvard Medical ...

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 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.

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 ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Enzyme engineering is a significant tool for fine-tuning enzymes to alter their stability, activity, and specificity. Enzymes are highly versatile macromolecular biological catalysts that can ...

Proteins are life's molecular workhorses, doing everything from turning sunlight into food to fighting viruses. They are built from 20 different types of amino acid molecules, so even a small protein ...