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 …

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 …

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 launch of AlphaFold series has brought deep-learning techniques into the molecular
structural science. As another crucial problem, structure-based prediction of protein-ligand …

Accurately predicting drug-target binding affinity plays a vital role in accelerating drug
discovery. Many computational approaches have been proposed due to costly and time …

Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …

Protein–ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery.
Recently, various deep learning-based models predict binding affinity by incorporating the …

Acyltransferase from Mycobacterium smegmatis is a versatile enzyme, which catalyzes the
transesterification of esters in aqueous media due to a kinetic preference of the synthesis …

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

Protein (receptor)--ligand interaction prediction is a critical component in computer-aided
drug design, significantly influencing molecular docking and virtual screening processes …