Use of Catalyst Pharmacophore Models for Screening of Large Combinatorial Libraries

Abstract

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.

Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes?

“…13). This descriptor takes into account the improvement of the hit rate by a VS protocol compared to a random selection [70,73,84,85].…”

Pharmacophore modeling and parallel screening for PPAR ligands

“…For these purposes, the 31 PPAR-a subtype selective agonists, the 135 PPAR-c subtype selective agonists, the five PPAR-c partial agonists and the 17 PPAR-d subtype selective agonists of the PPAR ligand test set were utilized as subsets of actives for the corresponding PPAR-a, PPAR-c, and PPAR-d agonist models and therefore included into the virtual database. According to references [23][24][25], the enrichment factor was calculated using the following equation:…”

Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads

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