Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains
use AI, including molecular simulation for drug discovery. In this review, we provide an …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Efficient identification of drug mechanisms of action remains a challenge. Computational
docking approaches have been widely used to predict drug binding targets; yet, such …

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

Molecular docking has been widely employed as a fast and inexpensive technique in the
past decades, both in academic and industrial settings. Although this discipline has now had …

Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …

Accurately predicting protein–ligand binding affinities is an important problem in
computational chemistry since it can substantially accelerate drug discovery for virtual …

Currently, molecular docking is becoming a key tool in drug discovery and molecular
modeling applications. The reliability of molecular docking depends on the accuracy of the …

Abstract Structure-based virtual screening (SBVS) via docking has been used to discover
active molecules for a range of therapeutic targets. Chemical and protein data sets that …

Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …