New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …

Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …

Despite the success of pretrained natural language processing (NLP) models in various
fields, their application in computational biology has been hindered by their reliance on …

Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …

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 …

In recent years, investigations on molecular interaction mechanisms between food proteins
and ligands have attracted much interest. The interaction mechanisms can supply much …

Highlights•Basic principles of commonly used deep learning architectures.•Drug–target
interactions based deep learning approaches for drug repositioning.•Heterogeneous …

There is great interest to develop artificial intelligence-based protein–ligand binding affinity
models due to their immense applications in drug discovery. In this paper, PointNet and …

Accurately predicting the binding affinity between proteins and ligands is crucial in drug
screening and optimization, but it is still a challenge in computer-aided drug design. The …

The launch of AlphaFold series has brought deep-learning techniques into the molecular
structural science. As another crucial problem, structure-based prediction of protein-ligand …