There is great interest to develop artificial intelligence-based protein–ligand binding affinity
models due to their immense applications in drug discovery. In this paper, PointNet and …

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 …

Accurate prediction of binding affinity between protein and ligand is a very important step in
the field of drug discovery. Although there are many methods based on different …

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient
and successful rational drug design. Therefore, many binding affinity prediction methods …

Prediction of protein-ligand interactions is a critical step during the initial phase of drug
discovery. We propose a novel deep-learning-based prediction model based on a graph …

Biomolecular recognition between ligand and protein plays an essential role in drug
discovery and development. However, it is extremely time and resource consuming to …

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 …

Computational drug discovery provides an efficient tool for helping large-scale lead
molecule screening. One of the major tasks of lead discovery is identifying molecules with …

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 …

Background Computer-aided drug design provides an effective method of identifying lead
compounds. However, success rates are significantly bottlenecked by the lack of accurate …