Bridging the technical divide in biological engineering Co-founders Tristan Bepler and Tim Lu developed the platform to ...

New platform gives researchers access to the quantity and quality of antibody-antigen affinity and structural data required for next-generation protein engineering models. Atlas by the Numbers Atlas ...

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 ...

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.

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.

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

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 ...

Stanford researchers have developed Microbe-Independent Deep Assembly and Screening – MIDAS – a polymerase chain ...

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 ...