Computational prediction of protein structure has been pursued intensely for decades,
motivated largely by the goal of using structural models for drug discovery. Recently …

Proteins are the molecular machinery of the human body, and their malfunctioning is often
responsible for diseases, making them crucial targets for drug discovery. The three …

The drug discovery process involves designing compounds to selectively interact with their
targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs …

A crucial component in structure-based drug discovery is the availability of high-quality three-
dimensional structures of the protein target. Whenever experimental structures were not …

AlphaFold2 is a promising new tool for researchers to predict protein structures and
generate high‐quality models, with low backbone and global root‐mean‐square deviation …

Artificial intelligence-based protein structure prediction methods such as AlphaFold have
revolutionized structural biology. The accuracies of these predictions vary, however, and …

Most proteins fold into 3D structures that determine how they function and orchestrate the
biological processes of the cell. Recent developments in computational methods for protein …

Highlights•The majority of proteins encoded in a genome are accessible by structure
modelling.•Modelling can provide accurate target predictions for structure-based drug …

Determining the three-dimensional structure of proteins in their native functional states has
been a longstanding challenge in structural biology. While integrative structural biology has …

Machine learning-based protein structure prediction algorithms, such as RosettaFold and
AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of …