Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

The data sets available to train models for in silico drug discovery efforts are often small.
Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence …

Structure-based drug design is becoming an essential tool for faster and more cost-efficient
lead discovery relative to the traditional method. Genomic, proteomic, and structural studies …

Computational approaches to drug discovery can reduce the time and cost associated with
experimental assays and enable the screening of novel chemotypes. Structure-based drug …

Abstract BindingDB, www. bindingdb. org, is a publicly accessible database of experimental
protein-small molecule interaction data. Its collection of over a million data entries derives …

The global spread of COVID-19 has raised the importance of pharmaceutical drug
development as intractable and hot research. Developing new drug molecules to overcome …

Highlights•We review machine learning methods/tools relevant to ligand-based virtual
screening.•Machine learning methods classify compounds and predict new active …

BindingDB () is a publicly accessible database currently containing∼ 20 000 experimentally
determined binding affinities of protein–ligand complexes, for 110 protein targets including …

Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid
bias, decoys should resemble ligands physically, so that enrichment is not simply a …

Ligand docking is a widely used approach in virtual screening. In recent years a large
number of publications have appeared in which docking tools are compared and evaluated …