Recent developments in deep learning, coupled with an increasing number of sequenced
proteins, have led to a breakthrough in life science applications, in particular in protein …

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

The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and
design. Traditional experiments are very expensive and time-consuming. Recently, deep …

In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our
approach to complex data. This review explores the attention mechanism and its extended …

Drug-target affinity prediction is a challenging task in drug discovery. The latest
computational models have limitations in mining edge information in molecule graphs …

Motivation Accurate prediction of drug–target binding affinity (DTA) is crucial for drug
discovery. The increase in the publication of large-scale DTA datasets enables the …

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 …

The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …

By accelerating time-consuming processes with high efficiency, computing has become an
essential part of many modern chemical pipelines. Machine learning is a class of computing …

The development of new drugs addressing serious mental health and other disorders
should avoid the psychedelic experience. Analogs of psychedelic drugs can have clinical …