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 …

De novo drug design is a stationary way to build novel ligands in the confined pocket of
receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation …

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …

Motivation Computational approaches for identifying the protein–ligand binding affinity can
greatly facilitate drug discovery and development. At present, many deep learning-based …

Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …

Motivation The identification of compound–protein interactions (CPIs) is an essential step in
the process of drug discovery. The experimental determination of CPIs is known for a large …

Motivation Accurate and rapid prediction of protein–ligand binding affinity is a great
challenge currently encountered in drug discovery. Recent advances have manifested a …

Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …

The accurate identification of Drug–Target Interactions (DTIs) remains a critical turning point
in drug discovery and understanding of the binding process. Despite recent advances in …

Computationally predicting drug-target binding affinity (DTA) has attracted increasing
attention due to its benefit for accelerating drug discovery. Currently, numerous deep …