Deep learning using neural networks relies on a class of machine-learnable models
constructed using 'differentiable programs'. These programs can combine mathematical …

Molecular docking has become an essential part of a structural biologist's and medicinal
chemist's toolkits. Given a chemical compound and the three-dimensional structure of a …

Protein-ligand blind docking is a powerful method for exploring the binding sites of receptors
and the corresponding binding poses of ligands. It has seen wide applications in …

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

Proteolysis-targeting chimeras (PROTACs) have emerged as effective tools to selectively
degrade disease-related proteins by using the ubiquitin-proteasome system. Developing …

Protein–ligand docking is an essential method in computer-aided drug design and structural
bioinformatics. It can be used to identify active compounds and reveal molecular …

Central in drug design is the identification of biomolecules that uniquely and robustly bind to
a target protein, while minimizing their interactions with others. Accordingly, precise binding …

The expertise accumulated in deep neural network-based structure prediction has been
widely transferred to the field of protein–ligand binding pose prediction, thus leading to the …

Kinases have been the focus of drug discovery programs for three decades leading to over
70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 …

High-quality protein–ligand complex structures provide the basis for understanding the
nature of noncovalent binding interactions at the atomic level and enable structure-based …