GRID-based three-dimensional pharmacophores II: PharmBench, a benchmark data set for evaluating pharmacophore...

Area of Science:

• Computational chemistry
• Drug discovery
• Structural bioinformatics

Background:

• Pharmacophore elucidation is crucial for drug discovery.
• Existing validation datasets are limited.
• A need exists for a standardized, experimental gold standard dataset.

Purpose of the Study:

• To create a comprehensive benchmark dataset (PharmBench) for pharmacophore elucidation.
• To establish objective measures for evaluating pharmacophore elucidation methods.
• To validate the FLAPpharm method using the new benchmark.

Main Methods:

• Assembled a dataset of 960 ligands across 81 targets, aligned by cocrystallized protein structures.
• Utilized 2D structures to remove conformational bias.
• Developed three objective performance metrics: bioactive conformation identification, alignment accuracy, and pharmacophoric field similarity.

Main Results:

• FLAPpharm identified bioactive conformations for 67% of ligands.
• FLAPpharm achieved a 67% success rate on PharmBench datasets using a root mean square (rms)-derived metric.
• Further analysis revealed an 83% overall success rate, indicating pharmacophorically reasonable models among initially unsuccessful ones.

Conclusions:

• The PharmBench dataset provides a robust experimental gold standard for pharmacophore elucidation.
• FLAPpharm shows strong performance in identifying and aligning bioactive ligand conformations.
• PharmBench and its associated web service offer a valuable community resource for method development and validation.