DeepMind (United Kingdom)

Highly accurate protein structure prediction with AlphaFold

Abstract Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort ...

Mastering the game of Go with deep neural networks and tree search

Mastering the game of Go without human knowledge

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Abstract The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in prote...

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Abstract The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure pred...

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation al...

Protein complex prediction with AlphaFold-Multimer

While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain prote...

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-o...

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular ...

Highly accurate protein structure prediction for the human proteome