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 …

Binding affinity optimization is crucial in early-stage drug discovery. While numerous
machine learning methods exist for predicting ligand potency, their comparative efficacy …

Protein--ligand interaction (PLI) prediction is critical in drug discovery, aiding the
identification and enhancement of molecules that effectively bind to target proteins. Despite …

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 …

Accurately predicting protein–ligand binding affinities is crucial for determining molecular
properties and understanding their physical effects. Neural networks and transformers are …

Introduction: The ability to discriminate among ligands binding to the same protein target in
terms of their relative binding affinity lies at the heart of structure-based drug design. Any …

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

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

Evaluating the protein–ligand binding affinity is a substantial part of the computer-aided drug
discovery process. Most of the proposed computational methods predict protein–ligand …

The cornerstone of computational drug design is the calculation of binding affinity between
two biological counterparts especially a chemical compound, ie a ligand, and a protein …