In the past, conventional drug discovery strategies have been successfully employed to
develop new drugs, but the process from lead identification to clinical trials takes more than …

Highlights•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint
and similarity analysis.•Machine learning models for virtual screening.•Future challenges …

Drug-like compounds are most of the time denied approval and use owing to the
unexpected clinical side effects and cross-reactivity observed during clinical trials. These …

High-throughput and virtual screening are important components of modern drug discovery
research. Typically, these screening technologies are considered distinct approaches, as …

Chemoinformatics draws upon techniques from many disciplines including computer
science, mathematics, computational chemistry and data visualisation to tackle these …

Increased availability of large repositories of chemical compounds is creating new
challenges and opportunities for the application of machine learning methods to problems in …

The maximum common subgraph (MCS) problem has become increasingly important in
those aspects of chemoinformatics that involve the matching of 2D or 3D chemical …

Fingerprint-based similarity searching is widely used for virtual screening when only a single
bioactive reference structure is available. This paper reviews three distinct ways of carrying …

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology.
Similar to the read across procedure in toxicological risk assessment, lazar creates local …

A binary linear programming formulation of the graph edit distance for unweighted,
undirected graphs with vertex attributes is derived and applied to a graph recognition …