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 …

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

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 …

Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …

Docking methodology aims to predict the experimental binding modes and affinities of small
molecules within the binding site of particular receptor targets and is currently used as a …

Docking tools to predict whether and how a small molecule binds to a target can be applied
if a structural model of such target is available. The reliability of docking depends, however …

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify
hits: when applied successfully, it can be much faster and cheaper than experimental high …

Abstract Structure-based virtual screening (SBVS) has been widely applied in early-stage
drug discovery. From a problem-centric perspective, we reviewed the recent advances and …

Molecular docking predicts whether and how small molecules bind to a macromolecular
target using a suitable 3D structure. Scoring functions for structure‐based virtual screening …

The aim of docking is to accurately predict the structure of a ligand within the constraints of a
receptor binding site and to correctly estimate the strength of binding. We discuss, in detail …