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 ...

(L) Senior co-corresponding author M. Madan Babu, PhD, FRS, St. Jude Senior Vice President of Data Science, Chief Data Scientist, Center of Excellence for Data-Driven Discovery director and Department ...

In a recent study published in the journal Nature Machine Intelligence, researchers developed "DeepGO-SE," a method to predict gene ontology (GO) functions from protein sequences using a large, ...

Scientists at St. Jude Children's Research Hospital have created a database that provides updated predicted structures on a regular basis, ensuring scientists can work with the most current ...

A new artificial intelligence (AI) tool that draws logical inferences about the function of unknown proteins promises to help scientists unravel the inner workings of the cell. Developed by KAUST ...

Researchers present BioEmu – a new AI model that rapidly and accurately predicts the full range of shapes a protein can adopt, offering a faster, cheaper alternative to traditional molecular ...

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

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 ...

Dr. Simon Sumer explains how AI‑driven protein design is shortening development cycles, improving screening efficiency, and ...