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

Abstract

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.

Latest developments in molecular docking: 2010–2011 in review

“…Lin and Tseng described LeadOp, a structure-based de novo lead optimization process, which is based on the decomposition of a compound into fragments. [123] Each fragment is evaluated via a predocked fragment database, which ranks fragments according to specific interactions with the receptor. Fragments, which contribute least to the binding, are then replaced and reassembled, resulting in a new compound.…”

A reverse combination of structure-based and ligand-based strategies for virtual screening

“…A fragment-based approach displaying similarities with our own has been published recently [25], although it appears to be more focused on the optimization of query molecules because the ligand database is decomposed into fragments that are evaluated for binding affinity using docking and scoring. Thereafter, those fragments exhibiting the lowest affinities are replaced by new ones and the affinity is re-calculated.…”

Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications

“…Traditionally, lead optimization relies heavily on the past experience of medicinal chemists and existing synthetic guidelines . With the accumulation of experimental data, more databases and computational tools have been developed and applied to lead optimization, such as bioisosteric databases, intermolecular interaction databases, and conformational landscape prediction tools as examples. − Several systematic approaches, such as Topliss Scheme, Craig Plot, Hansch, and Free Wilson, have been proposed to summarize analogue synthesis knowledge, physical properties–chemical structure correlation, and biological activity–chemical structure correlation. − …”