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

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 …

A few decades ago, drug discovery and development were limited to a bunch of medicinal
chemists working in a lab with enormous amount of testing, validations, and synthetic …

Informative representation of molecules is a crucial prerequisite in AI-driven drug design and
discovery. Pharmacophore information including functional groups and chemical reactions …

Highlights•Artificial intelligence can combat AMR by discovering antibiotic alternatives.•AI is
fast, cost-efficient, minimum labour dependant strategy.•The chances of failure in AI based …

Background: Cell–cell communication in a tumor microenvironment is vital to tumorigenesis,
tumor progression and therapy. Intercellular communication inference helps understand …

Drug targets are specific molecules in biological tissues and body fluids that interact with
drugs. Drug target discovery is a key component of drug discovery and is essential for the …

Motivation In recent years, a large number of biological experiments have strongly shown
that miRNAs play an important role in understanding disease pathogenesis. The discovery …

Drug–target interaction (DTI) prediction is an essential step in drug repositioning. A few
graph neural network (GNN)-based methods have been proposed for DTI prediction using …

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 …