The importance of drug design cannot be overemphasized. Recently, artificial intelligence
(AI) based drug design has begun to gain momentum due to the great advancement in …

When designing a machine learning‐based scoring function, we access a limited number of
protein‐ligand complexes with experimentally determined binding affinity values …

Computational approaches to the characterization and prediction of compound-protein
interactions have a long research history and are well established, driven primarily by the …

Determination of protein–ligand binding affinity (PLA) is a key technological tool in hit
discovery and lead optimization, which is critical to the drug development process. PLA can …

The prediction of the strengths of drug–target interactions, also called drug–target binding
affinities (DTA), plays a fundamental role in facilitating drug discovery, where the goal is to …

Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the
sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models …

In the drug discovery process and repurposing of existing drugs, accurately identifying
ligands with high binding affinity to proteins is a very critical step. However, it sinks a lot of …

Drug-target binding affinity (DTA) is a critical criterion for drug screening. Accurate affinity
prediction will significantly cut the cost of new drug development and accelerate the drug …

Biomolecular interaction recognition between ligands and proteins is an essential task,
which largely enhances the safety and efficacy in drug discovery and development stage …

Predicting protein-ligand binding affinity is important in areas such as drug discovery, gene
regulation and signal transduction. The DTA (Drug-Target Affinity) method based on protein …