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 …

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

Predicting the functional sites of a protein from its structure, such as the binding sites of small
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …

Motivation Gaining structural insights into the protein–protein interactome is essential to
understand biological phenomena and extract knowledge for rational drug design or protein …

We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy
and residue-residue distance signed error in protein models and uses these predictions to …

Computational methods that operate on three-dimensional molecular structure have the
potential to solve important questions in biology and chemistry. In particular, deep neural …

Motivation Proteins are ubiquitous molecules whose function in biological processes is
determined by their 3D structure. Experimental identification of a protein's structure can be …

Motivation Many important cellular processes involve physical interactions of proteins.
Therefore, determining protein quaternary structures provide critical insights for …

Motivation Inter-protein (interfacial) contact prediction is very useful for in silico structural
characterization of protein–protein interactions. Although deep learning has been applied to …