Protein function prediction and annotation represent critical challenges in the post‐genomic era. As high‐throughput sequencing continues to generate vast amounts of protein data, computational ...

This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational ...

A newly developed generative AI model is helping researchers explore protein dynamics with increased speed. The deep learning system, called BioEmu, predicts the full range of conformations a protein ...

Advances in generative AI, automation, and high‑throughput experimentation are rapidly changing protein engineering, enabling faster design, stability prediction, and functional optimization. New ...

On Wednesday, the Nobel Committee announced that it had awarded the Nobel Prize in chemistry to researchers who pioneered major breakthroughs in computational chemistry. These include two researchers ...

A team at Rice University has built a lab platform that can map the activity of more than 10 million protein variants in a ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has introduced CGSchNet, a machine-learned coarse-grained (CG) model that can ...

Recent advances in high-throughput proteomics enable the measurement of thousands of proteins simultaneously, offering ...

Researchers have developed a new artificial intelligence (AI) model that can more accurately predict how proteins interact ...

This review examines how high-throughput proteomics is expanding precision medicine by improving biomarker discovery, disease ...

At AACR 2026, researchers discussed the promise and challenges of bringing AI-powered tools into cancer research and clinical ...