The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Prediction of protein structure from sequence has been intensely studied for many decades,
owing to the problem's importance and its uniquely well-defined physical and computational …

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of
Structure Prediction (CASP14) conference. We explored network architectures that …

Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …

Structure prediction for proteins lacking homologous templates in the Protein Data Bank
(PDB) remains a significant unsolved problem. We developed a protocol, CI-TASSER, to …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

Language models have recently emerged as a powerful machine-learning approach for
distilling information from massive protein sequence databases. From readily available …