Approaching Pharmacological Space: Events and Components.

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• The pharmacological space encompasses dynamic molecular events influencing bioactivity, metabolism, and toxicity.
• It accounts for structural flexibility, property variability, and mutual adaptability in molecular recognition.
• Traditional methods often focus on the most probable molecular state, neglecting dynamic behaviors.

Purpose of the Study:

• To introduce and validate the concept of pharmacological space for computational drug discovery.
• To demonstrate the application of pharmacological space parameters in enhancing virtual screening and toxicity prediction.
• To present a computational workflow combining docking and rescoring for descriptor enrichment.

Main Methods:

• Calculation of state-dependent ligand- and structure-based descriptors across all monitored molecular states.
• Evaluation of descriptor spaces using dynamic parameters like mean and range values.
• Integration of docking simulations and rescoring analyses, followed by consensus modeling using the EFO approach.

Main Results:

• Inclusion of pharmacological space parameters significantly improved performance in virtual screening and toxicity prediction.
• Combining different binding modes and target structures yielded beneficial effects.
• The proposed computational workflow demonstrated general applicability across various targets and docking engines.

Conclusions:

• The pharmacological space offers a more comprehensive approach to understanding ligand-target interactions.
• This methodology enhances the accuracy and performance of structure-based virtual screening and toxicity prediction.
• The developed computational workflow provides a robust and broadly applicable tool for drug discovery.