Conformational sampling with stochastic proximity embedding and self-organizing superimposition: establishing...

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Drug Discovery

Background:

• Stochastic proximity embedding (SPE) and self-organizing superimposition (SOS) are novel conformational sampling methods.
• Previous studies involved exhaustive searches, which are impractical for large-scale applications like virtual screening.

Purpose of the Study:

• To evaluate the performance of SPE and SOS under varying sampling levels.
• To identify optimal search protocols for generating diverse, chemically sensible conformations.
• To assess the probability of sampling biologically active space with limited trials.

Main Methods:

• Examined SPE and SOS performance with different parameter settings.
• Varied the number of sampled conformations per molecule.
• Minimized generated conformations using molecular mechanics force fields.

Main Results:

• Both SPE and SOS are highly competitive, yielding satisfactory results with as few as 500 conformations per molecule.
• Performance further improved after minimizing conformations to remove strain.
• The methods demonstrated effectiveness for both high- and low-throughput modeling tasks.

Conclusions:

• SPE and SOS are efficient and reliable for generating diverse molecular conformations.
• These methods are well-suited for virtual screening and routine molecular modeling.
• Optimized protocols enable effective sampling of biologically relevant conformational space.