Recently, attention mechanism and derived models have gained significant traction in drug
development due to their outstanding performance and interpretability in handling complex …

Motivation Exploring drug–protein interactions (DPIs) provides a rapid and precise approach
to assist in laboratory experiments for discovering new drugs. Network-based methods …

Computationally predicting drug-target binding affinity (DTA) has attracted increasing
attention due to its benefit for accelerating drug discovery. Currently, numerous deep …

Identification of RNA–small molecule binding sites plays an essential role in RNA-targeted
drug discovery and development. These small molecules are expected to be leading …

Accurate and effective drug–target interaction (DTI) prediction can greatly shorten the drug
development lifecycle and reduce the cost of drug development. In the deep-learning-based …

Compound–protein interactions (CPI) play significant roles in drug development. To avoid
side effects, it is also crucial to evaluate drug selectivity when binding to different targets …

Proteins interact with diverse ligands to perform a large number of biological functions, such
as gene expression and signal transduction. Accurate identification of these protein–ligand …

Protein-protein interactions govern a wide range of biological activity. A proper estimation of
the protein-protein binding affinity is vital to design proteins with high specificity and binding …

The prediction of the drug-target affinity (DTA) plays an important role in evaluating
molecular druggability. Although deep learning-based models for DTA prediction have been …

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited
amount of experimental data, developing accurate sequence-based predictors of substrate …