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Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

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Parameterization and conformational sampling effects in pharmacophore multiplet searching.

Peter C Fox¹, Philippa R N Wolohan, Edmond Abrahamian

¹Tripos International, 1699 South Hanley Road, St. Louis, Missouri 63144, USA.

Journal of Chemical Information and Modeling

|December 5, 2008

Summary

This summary is machine-generated.

Pharmacophore patterns, represented by bitsets, are crucial for molecular modeling. Optimizing parameters like conformational sampling is key for accurate similarity searching and drug discovery.

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Area of Science:

Computational chemistry and cheminformatics
Molecular modeling and drug discovery

Background:

Pharmacophore patterns in ligands are characterized by pharmacophore multiplets.
Bitsets (fingerprints) encoding these multiplets serve as molecular descriptors in various applications, including ligand alignment and flexible searching.
Compressed bitmap representations enable integration into high-throughput technologies.

Purpose of the Study:

To explore the impact of parameter variations on pharmacophore multiplet searching performance.
To introduce a novel similarity metric, the asymmetric stochastic cosine, for database searching.
To identify optimal strategies for improving discrimination in pharmacophore-based searches.

Main Methods:

Analysis of within- and between-class similarity across seven pharmacological classes.
Evaluation of key parameters: edge length binning granularity, multiplet weighting, and hypothesis bit count.
Investigation of similarity metrics and conformational sampling regimes for bitmap comparisons.

Main Results:

The study reveals that fewer conformers yield more discriminating bitmaps, potentially due to 2D connectivity influencing 3D structure.
Systematic conformational sampling can introduce bias and should be avoided.
Consolidating information from multiple active ligands or defining single bioactive conformations enhances discrimination.

Conclusions:

Parameter optimization, particularly in conformational sampling, is critical for effective pharmacophore searching.
The asymmetric stochastic cosine offers a unique approach for matching query hypotheses derived from multiple ligands.
Careful consideration of conformational representation is essential for robust and unbiased molecular similarity assessments.