Title:
Conformational Consensus as a Method to Distinguish Agonists from
Antagonists: Identification of Selective Conformations and Pharmacophore
Model Generation

Author:
Gregory J. Tawa
Wyeth Research

Abstract:
With the explosive growth in chemical data generated from HTS and
subsequent lead optimization efforts it has become difficult for chemists
to identify useful pharmacophore models for the targets under study.
Pharmacophore identification can be a computationally demanding process,
involving analysis of large data sets.   Recently we have developed a
method called PaccMan in which distributions of conformations are compared
across a series of potent GPCR ligands.  Through systematic
shape/pharmacophore searching, we find that conformations exist that are
shared predominantly by agonists or antagonists.  Once a standard database
of ligands is created, we can then predict the likely functional activity
of a new structure.  The method also allows us to determine which
selective conformations can best be overlapped to generate 3D
Pharmacophore models that can be used either in lead optimization efforts
or as starting points for ligand-based virtual screens.  This method is
demonstrated with a series of 5HT1A agonists and antagonists and with a
set of small molecules that selectively bind to various targets.  A
comprehensive suite of programs designed to manage the large amount of
data generated by the systematic searches will be described [PaccMan -
Pharmacophore Analysis by Conformational Consensus - Management Tool].
The suite of programs analyzes report files generated from multiple ROCS
searches. The query and reference databases used in the ROCS searches are
typically multi-molecule multi-conformational in nature and are generated
using OMEGA.