| Literature DB >> 11143555 |
A R Leach1, R A Bryce, A J Robinson.
Abstract
Traditional de novo design algorithms are able to generate many thousands of ligand structures that meet the constraints of a protein structure, but these structures are often not synthetically tractable. In this article, we describe how concepts from structure-based de novo design can be used to explore the search space in library design. A key feature of the approach is the requirement that specific templates are included within the designed structures. Each template corresponds to the "central core" of a combinatorial library. The template is positioned within an acyclic chain whose length and bond orders are systematically varied, and the conformational space of each structure that results (core plus chain) is explored to determine whether it is able to link together two or more strongly interacting functional groups or pharmacophores located within a protein binding site. This fragment connection algorithm provides "generic" 3D molecules in the sense that the linking part (minus the template) is built from an all-carbon chain whose synthesis may not be easily achieved. Thus, in the second phase, 2D queries are derived from the molecular skeletons and used to identify possible reagents from a database. Each potential reagent is checked to ensure that it is compatible with the conformation of its parent 3D conformation and the constraints of the binding site. Combinations of these reagents according to the combinatorial library reaction scheme give product molecules that contain the desired core template and the key functional/pharmacophoric groups, and would be able to adopt a conformation compatible with the original molecular skeleton without any unfavorable intermolecular or intramolecular interactions. We discuss how this strategy compares with and relates to alternative approaches to both structure-based library design and de novo design.Entities:
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Year: 2000 PMID: 11143555 DOI: 10.1016/s1093-3263(00)00062-0
Source DB: PubMed Journal: J Mol Graph Model ISSN: 1093-3263 Impact factor: 2.518