New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …

A typical drug discovery project involves identifying active compounds with significant
binding potential for selected disease-specific targets. Experimental high-throughput …

Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …

The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …

Accurate in silico prediction of protein–ligand binding affinity is important in the early stages
of drug discovery. Deep learning-based methods exist but have yet to overtake more …

The core of large-scale drug virtual screening is to select the binders accurately and
efficiently with high affinity from large libraries of small molecules in which non-binders are …

Elucidating protein–ligand interaction is crucial for studying the function of proteins and
compounds in an organism and critical for drug discovery and design. The problem of …

The goal of drug discovery is to uncover new molecules with specific chemical properties
that can be used to cure diseases. With the accessibility of machine learning techniques, the …

Binding MOAD is a database of protein–ligand complexes and their affinities with many
structured relationships across the dataset. The project has been in development for over 20 …

Accurately predicting the binding affinity between proteins and ligands is crucial in drug
screening and optimization, but it is still a challenge in computer-aided drug design. The …