The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Although there has been considerable progress in molecular property prediction in
computer-aided drug design, there is a critical need to have fast and accurate models. Many …

With the increasing pollution of the planet, the search for natural multifunctional alternatives
to petroleum-based plastics has assumed to be a great important proposition …

The expertise accumulated in deep neural network-based structure prediction has been
widely transferred to the field of protein–ligand binding pose prediction, thus leading to the …

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 …

Water network rearrangement from the ligand-unbound state to the ligand-bound state is
known to have significant effects on the protein–ligand binding interactions, but most of the …

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are
involved in the transportation of diverse solute ions and small molecules into cells. There are …

Structure-based drug design (SBDD), which utilizes the three-dimensional geometry of
proteins to identify potential drug candidates, is becoming increasingly vital in drug …

We introduce a deep learning‐based ligand pose scoring model called zPoseScore for
predicting protein–ligand complexes in the 15th Critical Assessment of Protein Structure …

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