Development of a representative compound library for selecting fragment candidate combinations of fragment-based drug design

Abstract

In Fragment-Based Drug Design (FBDD), active fragments are identified using experimental methods and then medicinal chemists combine them to design new compounds to be synthesized. In silico fragment identification methods may find a number of fragment ligands on the active site, resulting in the combinatorial explosion of fragment sets that are combined to design new compounds. Previously, we proposed the knowledge-based method to prioritize such fragment sets based on the number of hits on the substructure search against a reference database. Although it is possible for the method to select hopeful fragment lists, the calculation will be too expensive if the number of compounds in the reference compound library is very large. In this study, we try to construct a subset library (informative compound library) enough representative of the original reference library by clustering and filtering the compounds contained in the database. It can be expected to improve the performance of our fragment-combination selection and also may be used with other FBDD methods.