Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic
properties remains a formidable challenge for drug discovery. Deep learning provides us with …

p300 and CREB-binding protein (CBP) are ubiquitously expressed pleiotropic lysine
acetyltransferases and play a key role as transcriptional co-activators that are essential for a …

A typical output of a metabolomic experiment is a peak table corresponding to the intensity
of measured signals. Peak table processing, an essential procedure in metabolomics, is …

Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity
(T) modelling as a tool for rational drug design has received considerable attention from …

Artificial intelligence (AI) is booming. Among various AI approaches, generative models have
received much attention in recent years. Inspired by these successes, researchers are now …

Abrogating tumor angiogenesis by inhibiting vascular endothelial growth factor receptor‐2 (
VEGFR 2) has been established as a therapeutic strategy for treating cancer. However, …

Developing new drugs remains prohibitively expensive, time-consuming, and often involves
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …

PROteolysis TArgeting Chimeras (PROTACs) has been exploited to degrade putative protein
targets. However, the antitumor performance of PROTACs is impaired by their insufficient …

Highlights • GNN has attracted wide attention from the field of designing drug molecules. •
The applications of GNN in molecule scoring, molecule generation and optimization, and …