Structure-Based Fragment Hopping for Lead Optimization Using Predocked Fragment Database

In this work, we describe a structure-based de novo optimization process, called "LeadOp" (short for lead optimization), that decomposes a compound into fragments of different molecular components either by chemical or user-defined rules. Each fragment is evaluated through a predocked fragment database that ranks fragments according to specific fragment-receptor binding interactions, replacing fragments that contribution the least to binding and finally reassembling the fragments to form a new ligand. The fundamental idea is to replace "bad" fragments of a ligand with "good" fragments while leaving the core of the ligand intact, thus improving the compound's activity. The molecular fragments were selected from a collection of 27,417 conformers that are the fragments of compounds in the DrugBank database. The collection of molecular fragments are docked to the target's binding site and evaluated using group efficiency (calculated binding affinity divided by the number of heavy atoms), and the "strongest" binder is selected. The LeadOp method was tested with two biomolecular systems: mutant B-Raf kinase and human 5-lipoxygenase. The LeadOp methodology was able to optimize the query molecules and systematically developed improved analogs for each of our example systems.