Title: Substructural Analysis in Drug Discovery
Volume: 3
Issue: 1
Author(s): Hugo O. Villar, Mark R. Hansen and Richard Kho
Affiliation:
Keywords:
Substructure analysis, maximum common substructure, fragment-based discovery, chemical data management, chemoinformatics, data mining
Abstract: The dominant paradigm in drug discovery emphasizes techniques that generate large amounts of data. What was possible by simple inspection in the past, nowadays cannot be effectively achieved without the aid of informatics techniques. In this context substructural analysis techniques are increasing their role in the organization and management of information generated. Advances in the field of substructure analysis have expanded the applicability of substructural analysis in multiple fronts in early lead discovery and optimization. It can be applied beyond the management of information, including compound library design and virtual screening to structure activity relationships. The relationships between chemical substructures and drug-like properties also aid in developing more robust rationales for fragment-based approaches for lead discovery, predictive toxicology, and elucidation of pharmacokinetic properties.A review of recent developments in substructure analysis in a broad range of areas in drug discovery is presented. The focus is on the application of substructural analysis in computational chemistry for drug design and the methods used to identify substructures in a chemical database, as well as their relation to fragment-based drug discovery. The discussion shows the benefits of substructural analysis to the drug discovery process and gives impetus to further advancement of substructure analysis techniques.
