Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques

Abstract

SummaryPharmacophore methods provide a way of establishing a structure-activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-thefly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an

Incorporating partial matches within multiobjective pharmacophore identification

“…In our previous work [9] we were able to demonstrate the benefits of the multiobjective optimisation approach over a traditional GA, however, we also highlighted a limitation in the method which also applies to GASP. This is the restriction that each pharmacophore point generated must be mapped to a feature in every ligand.…”

“…We have extended the multiobjective genetic algorithm (MOGA) described previously [9] to allow the pharmacophore hypotheses identified to include partial matches. A partial match is defined as a pharmacophoric feature that is present in at least two, but not all, of the ligands in the set.…”

“…In a previous study, we have explored the use of a multiobjective optimisation method which aims to find a diverse set of pharmacophore hypotheses that are all plausible for a given set of ligands [9]. The algorithm explores the conformational space of the ligands simultaneously with their alignment.…”

“…The first generation of pharmacophore identification programs have been around for more than a decade [1][2][3][4]. However, recently some of the limitations of these early methods have been highlighted [5,6] and several new approaches are now beginning to appear that address some of these limitations [7][8][9][10].…”

Chemoinformatics at the University of Sheffield 2002–2014

“…To address limitations in GASP, the Sheffield group looked at modifying the genetic algorithm to a multiobjective evolutionary algorithm where the three objectives are handled independently rather than being combined into a single weighted function; [67] the rationale being that the objectives in GASP can be competing, for example, a better fit to a hypothesis may be achievable if the molecules are assumed to adopt higher energy conformations. In the multiobjective algorithm, Pareto ranking is used to find a set of optimal solutions that represent different compromises in the objectives, without the need to assign relative weights.…”