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.

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

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 their study published in the journal ...

Biotech advances from UT’s new Deep Proteins group are changing the game with help from artificial intelligence. Researchers study the three dimensional structures of molecules on a wall-sized video ...

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20100 ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin introduces a breakthrough in protein simulation. The study, published in the ...

A new method that successfully designs serine hydrolase enzymes capable of catalyzing ester hydrolysis with high efficiency, demonstrates a computational approach for creating de novo enzymes that ...

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 ...

Protein engineering is a powerful biotechnological process that focuses on creating new enzymes or proteins and improving the functions of existing ones by manipulating their natural macromolecular ...