Generation of reliable pharmacophore models is a key strategy in drug design. 
The quality of a pharmacophore model is known to depend on several factors, with 
the quality of the conformer sets used perhaps being one of the most important. 
The goal of this study was to compare different conformational analysis methods 
to determine if one was superior to the others for pharmacophore generation 
using Catalyst/HypoGen. The five methods selected were Catalyst/Fast, 
Catalyst/Best, Omega, Chem-X and MacroModel. Data sets for which Catalysts 
models had previously been published were selected using defined quality 
measures. Hypotheses were generated for each of the data sets and the 
performance of the different conformational analysis methods was compared using 
both quantitative (cost and correlation coefficients) and qualitative measures 
(by comparing the hypotheses in terms of the features present and their spatial 
relationships). Two main conclusions emerged from the study. First, it was not 
always possible to replicate the literature results. The reasons for these 
failures are explored in detail, and a template for use in publications that 
apply the Catalyst methodology is proposed. Second, the faster rule-based 
methods for conformational analysis give pharmacophore models that are just as 
good as, and in some cases better than, the models generated using the slower, 
more rigorous approaches.