Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

In silico tools, such as molecular docking, are widely applied to study interactions and
binding affinity of biological activity of proteins and peptides. However, restricted sampling of …

Predicting the binding structure of a small molecule ligand to a protein--a task known as
molecular docking--is critical to drug design. Recent deep learning methods that treat …

Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

Fragment-based drug discovery has been an effective paradigm in early-stage drug
development. An open challenge in this area is designing linkers between disconnected …

Illuminating interactions between proteins and small drug molecules is a long-standing
challenge in the field of drug discovery. Despite the importance of understanding these …

Protein complex formation is a central problem in biology, being involved in most of the cell's
processes, and essential for applications, eg drug design or protein engineering. We tackle …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

The goal of structure-based drug discovery is to find small molecules that bind to a given
target protein. Deep learning has been used to generate drug-like molecules with certain …

The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …