Drug-Membrane Permeability across Chemical Space

Area of Science:

• Computational chemistry and molecular modeling.
• Drug discovery and pharmaceutical sciences.
• Physical chemistry and membrane biophysics.

Background:

• Understanding drug permeation through lipid membranes is crucial for drug development.
• The vast chemical space of druglike compounds hinders comprehensive structure-permeability relationship analysis.
• Existing methods face challenges in predicting passive permeation rates accurately.

Purpose of the Study:

• To develop a computationally efficient method for predicting passive permeation rates of small druglike compounds.
• To establish a comprehensive structure-permeability relationship using simplified molecular descriptors.
• To enable inverse design for optimizing drug candidates based on predicted permeability.

Main Methods:

• Utilized a physics-based coarse-grained model to reduce chemical space complexity.
• Performed high-throughput coarse-grained (HTCG) simulations for extensive compound analysis.
• Derived a permeability surface based on bulk partitioning free energy and pKa.

Main Results:

• Successfully mapped permeability across a simplified chemical space.
• Identified key molecular descriptors (free energy, pKa) governing passive permeation.
• Established a direct correlation between chemical moieties and permeability coefficients for over 500,000 compounds.
• Demonstrated the feasibility of inverse design for permeability.

Conclusions:

• The coarse-grained modeling approach significantly reduces computational cost for exploring structure-property relationships.
• The derived permeability surface provides a powerful tool for predicting and optimizing drug permeation.
• Findings facilitate rational drug design by linking chemical structure to membrane permeability, with implications for drug synthesis.