Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …

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 …

Deep-learning language models have shown promise in various biotechnological
applications, including protein design and engineering. Here we describe ProGen, a …

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

Protein design aims to build novel proteins customized for specific purposes, thereby
holding the potential to tackle many environmental and biomedical problems. Recent …

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with
exceptionally high accuracy. Its implementation, however, lacks the code and data required …

We consider the problem of predicting a protein sequence from its backbone atom
coordinates. Machine learning approaches to this problem to date have been limited by the …

Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and …

Predicting the effects of coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …

Signal peptides (SPs) are short amino acid sequences that control protein secretion and
translocation in all living organisms. SPs can be predicted from sequence data, but existing …