Identifying native-like protein–ligand complexes (PLCs) from an abundance of docking
decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead …

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

The prediction of precise protein-ligand binding activities can accelerate drug discovery by
virtual screening-a computational technique that predicts whether a small molecule ligand is …

Classical scoring functions have reached a plateau in their performance in virtual screening
and binding affinity prediction. Recently, machine-learning scoring functions trained on …

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 …

Deep learning methods that predict protein–ligand binding have recently been used for
structure-based virtual screening. Many such models have been trained using protein …

Prediction of protein–ligand interactions (PLI) plays a crucial role in drug discovery as it
guides the identification and optimization of molecules that effectively bind to target proteins …

Protein–ligand interaction prediction presents a significant challenge in drug design.
Numerous machine learning and deep learning (DL) models have been developed to …

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 …

Predicting protein-ligand interactions using artificial intelligence (AI) models has attracted
great interest in recent years. However, data-driven AI models unequivocally suffer from a …