This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug
discovery and development, encapsulating its potentials, methodologies, real-world …

The tasks of drug-target interaction (DTI) and drug-target affinity (DTA) prediction play
important roles in the field of drug discovery. However, biological experiment-based …

Drug–target interactions (DTIs) play a key role in drug discovery and development as they
are critical in understanding the complex mechanisms of underlying drugs and their …

Background Drug-target binding affinity (DTA) prediction is important for the rapid
development of drug discovery. Compared to traditional methods, deep learning methods …

Motivation Accurately predicting the drug− target binding affinity (DTA) is crucial to drug
discovery and repurposing. Although deep learning has been widely used in this field, it still …

Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all
metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 …

Accurate prediction of drug–target affinity (DTA) is of vital importance in early-stage drug
discovery, facilitating the identification of drugs that can effectively interact with specific …

Drug discovery is a complicated process through which new therapeutics are identified to
prevent and treat specific diseases. Identification of drug–target interactions (DTIs) stands as …

Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for
modern drug discovery. Computational methods of DTA prediction, applied in the early …

In drug discovery, determining the binding affinity and functional effects of small-molecule
ligands on proteins is critical. Current computational methods can predict these protein …