Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …

Since the first revelation of proteins functioning as macromolecular machines through their
three dimensional structures, researchers have been intrigued by the marvelous ways the …

Although deep learning has revolutionized protein structure prediction, almost all
experimentally characterized de novo protein designs have been generated using …

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 …

Learning on 3D structures of large biomolecules is emerging as a distinct area in machine
learning, but there has yet to emerge a unifying network architecture that simultaneously …

Protein inverse folding has attracted increasing attention in recent years. However, we
observe that current methods are usually limited to the CATH dataset and the recovery …

Proteins with desired functions and properties are important in fields like nanotechnology
and biomedicine. De novo protein design enables the production of previously unseen …

The task of protein sequence design is central to nearly all rational protein engineering
problems, and enormous effort has gone into the development of energy functions to guide …

Several previously proposed deep learning methods to design amino acid sequences that
autonomously fold into a given protein backbone yielded promising results in computational …

Introduction: Precision medicine is the concept of treating diseases based on environmental
factors, lifestyles, and molecular profiles of patients. This approach has been found to …