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 (DL) has recently enabled unprecedented advances in one of the grand
challenges in computational biology: the half-century-old problem of protein structure …

Three billion years of evolution has produced a tremendous diversity of protein molecules,
but the full potential of proteins is likely to be much greater. Accessing this potential has …

Proteins are essential to life, and understanding their structure can facilitate a mechanistic
understanding of their function. Through an enormous experimental effort 1, 2, 3, 4, the …

Predicting molecular conformations from molecular graphs is a fundamental problem in
cheminformatics and drug discovery. Recently, significant progress has been achieved with …

AlphaFold2 and related computational systems predict protein structure using deep learning
and co-evolutionary relationships encoded in multiple sequence alignments (MSAs) …

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

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

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 …

The rational design of molecules with desired properties is a long-standing challenge in
chemistry. Generative neural networks have emerged as a powerful approach to sample …