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

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 ...

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 ...

The in silico protein design market offers key opportunities in AI-driven drug discovery, personalized medicine, and automation in biological research. With cloud-based simulation growth, partnerships ...

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.

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.

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 ...

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