Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural
networks such as deep neural networks or recurrent networks drive this area. Numerous …

Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …

Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …

Quantum computing has rapidly advanced in recent years due to substantial development in
both hardware and algorithms. These advances are carrying quantum computers closer to …

With the recent explosion in the size of libraries available for screening, virtual screening is
positioned to assume a more prominent role in early drug discovery's search for active …

Protein‐ligand interactions are a necessary prerequisite for signal transduction,
immunoreaction, and gene regulation. Protein‐ligand interaction studies are important for …

The drug development is generally arduous, costly, and success rates are low. Thus, the
identification of drug-target interactions (DTIs) has become a crucial step in early stages of …

With rapid advances in computer algorithms and hardware, fast and accurate virtual
screening has led to a drastic acceleration in selecting potent small molecules as drug …

Proteins interact with small molecules to modulate several important cellular functions. Many
acute diseases were cured by small molecule binding in the active site of protein either by …

Deep neural networks have achieved state-of-the-art accuracy at classifying molecules with
respect to whether they bind to specific protein targets. A key breakthrough would occur if …