Phys.org

Machine learning method improves accuracy of inverse protein folding for drug design

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments. Inverse protein folding is a critical ...
Nanowerk

Advancing protein simulation with Machine Learning

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 ...
Phys.org

Machine learning model predicts protein binding on gold nanoclusters

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.
EurekAlert!

Protein engineering advances unlock pathways to greener industrial biocatalysts

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 ...
EurekAlert!

Machine learning enables comprehensive prediction of the relative protein abundance of multiple proteins on the protein corona

The small size of nanoparticles is associated with several unique properties that mediate a profound impact on the development of many technological fields, including medicine. The nanoparticles in ...
The Conversation

Machine learning cracked the protein‑folding problem and won the 2024 Nobel Prize in chemistry

The 2024 Nobel Prize in chemistry recognized Demis Hassabis, John Jumper and David Baker for using machine learning to tackle one of biology’s biggest challenges: predicting the 3D shape of proteins ...