Conventional wet laboratory testing, validations, and synthetic procedures are costly and
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …

Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …

Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …

Motivation Drug–target interaction (DTI) prediction is a foundational task for in-silico drug
discovery, which is costly and time-consuming due to the need of experimental search over …

Predicting drug–target interaction is key for drug discovery. Recent deep learning-based
methods show promising performance, but two challenges remain: how to explicitly model …

Generative deep learning is accelerating de novo drug design, by allowing the generation of
molecules with desired properties on demand. Chemical language models–which generate …

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

The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is
a central parameter in studies of enzyme kinetics and cellular physiology. As measurements …

The prediction of drug-target affinity (DTA) plays an increasingly important role in drug
discovery. Nowadays, lots of prediction methods focus on feature encoding of drugs and …

Exiting computational models for drug–target binding affinity prediction have much room for
improvement in prediction accuracy, robustness and generalization ability. Most deep …