Conformational ensemble comparison for small molecules in drug discovery.

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Quantitative comparison of 3D molecular similarity is crucial for rational drug design.
• Existing methods often struggle to compare entire sets of molecular conformations (ensembles), especially when experimental structures are unavailable.

Purpose of the Study:

• To develop and validate a set of complementary scoring functions for comparing whole conformational ensembles of small molecules.
• To enable structure-based drug design insights even when precise 3D structures of molecules are not known.

Main Methods:

• Proposed three novel ensemble comparison scores: maximum pairwise similarity, proportion of matched conformations within a threshold, and average of the molecular shape descriptor 'σ-fct' over matched conformations.
• Utilized three case studies to demonstrate the utility of the scoring set, including conformational behavior analysis, activity-based comparisons of drug series, and virtual screening enrichment.

Main Results:

• The proposed scoring set effectively discriminated between different conformational behaviors of modified compounds.
• The scores successfully analyzed structure-activity relationships in GPR119 agonists and orexin-1 antagonists.
• Ensemble comparison against a reference compound enriched active compounds over inactive ones in a subset of the DUD·E dataset.

Conclusions:

• The developed ensemble comparison scores provide a valuable tool for rational drug design, particularly in scenarios lacking known 3D structures.
• These methods facilitate the analysis of molecular conformational ensembles and can aid in the identification and optimization of drug candidates.