insights from industryJeff HawkinsCEOQuantum-SiIn this interview, NewsMedical speaks with Jeff Hawkins, CEO of Quantum-Si, about the challenges of conventional proteomic methods, as well as how ...

Researchers from Facebook AI Research (FAIR) at Meta AI have published a paper in the journal Science detailing a machine-learning-created database of 617 million predicted protein structures. The ...

Proteoforms, the diverse molecular variants of proteins, are key to understanding cellular functions, disease mechanisms, and biomarker discovery in proteomics.

Researchers developed a new machine learning method that, given a relevant amino acid sequence, can automatically predict the location of a protein in any human cell line down to the single-cell level ...

This year’s Lasker Basic Medical Research Award recognizes the contributions of Demis Hassabis and John Jumper for their invention of the AlphaFold artificial intelligence (AI) system, which predicts ...

Machine learning (ML) and other AI- based computational tools have proven their prowess at predicting real-world protein structures. AlphaFold 2, an algorithm developed by scientists at DeepMind that ...

The process of protein identification typically begins with a bottom-up approach, where proteins are enzymatically digested—most commonly with trypsin—into smaller peptides. These peptides are ...

AI protein function prediction uses machine learning models trained on sequence and structural data to infer protein roles at ...

In the wee hours of an October morning, David Baker, a protein biologist at the University of Washington (UW), received the most-awaited phone call in a scientist’s career. Halfway around the world, ...