In silico prediction of the ligand binding pose to a given protein target is a crucial but
challenging task in drug discovery. This work focuses on blind flexible selfdocking, where …

Modeling the interaction between proteins and ligands and accurately predicting their
binding structures is a critical yet challenging task in drug discovery. Recent advancements …

Protein–ligand interactions are increasingly profiled at high-throughput, playing a vital role
in lead compound discovery and drug optimization. Accurate prediction of binding pose and …

Flexible modeling of the protein–ligand complex structure is a fundamental challenge for in
silico drug development. Recent studies have improved commonly used docking tools by …

The high-performance computational techniques have brought significant benefits for drug
discovery efforts in recent decades. One of the most challenging problems in drug discovery …

Predicting the binding sites of the target proteins plays a fundamental role in drug discovery.
Most existing deep-learning methods consider a protein as a 3D image by spatially …

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 …

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 …

Protein-ligand docking is an essential tool in structure-based drug design with applications
ranging from virtual high-throughput screening to pose prediction for lead optimization. Most …

Accurate prediction of protein–ligand interactions can greatly promote drug development.
Recently, a number of deep-learning-based methods have been proposed to predict protein …