Molecular Substructure Mining Approaches for Computer-Aided Drug Discovery A Review

Substructure mining is a well-established technique used frequently in drug discovery. Its aim is to discover and characterize interesting 2D substructures present in chemical datasets. The popularity of the approach owes a lot to the success of the structure-activity relationship practice, which states that biological properties of molecules are a result of molecular structure, and to expert medicinal chemists who tend to view, organize and treat chemical compounds as a collection of their substructural parts. Several substructure mining algorithms have been developed over the years to accommodate the needs of an ever changing drug discovery process. This paper reviews the most important of these algorithms and highlights some of their applications. Emphasis is placed on the recent developments in the field.