The global spread of COVID-19 has raised the importance of pharmaceutical drug
development as intractable and hot research. Developing new drug molecules to overcome …

The predictive performance of classical scoring functions (SFs) seems to have reached a
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …

Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …

The accurate identification of Drug–Target Interactions (DTIs) remains a critical turning point
in drug discovery and understanding of the binding process. Despite recent advances in …

Drug development is expensive, time-consuming, and has a high failure rate. In recent
years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery …

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …

Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …

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 …

Computational methods and recently modern machine learning methods have played a key
role in structure-based drug design. Though several benchmarking datasets are available …

Accurate prediction of the protein–ligand binding affinity (PLBA) with an affordable cost is
one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a …