Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …

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 …

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 …

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 …

Genomic and phenomic prediction (GP and PP, respectively) are innovative methods that
allow plant breeders to increase the productivity of crops. Traditional methods for conducting …

Background Several computational advances have been achieved in the drug discovery
field, promoting the identification of novel drug–target interactions and new leads. However …

It takes an average of 10-15 years to uncover and develop a new drug, and the process is
incredibly time-consuming, expensive, difficult, and ineffective. In recent years the dramatic …

A continuing challenge in modern medicine is the identification of safer and more efficacious
drugs. Precision therapeutics, which have one molecular target, have been long promised to …

Cancer nanotechnology takes advantage of nanoparticles to diagnose and treat cancer. The
use of natural and synthetic polymers for drug delivery has become increasingly popular …

Traditional drug discovery methods such as wet-lab testing, validations, and synthetic
techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have …