Energy-entropy multiscale cell correlation method to predict toluene-water log P in the SAMPL9 challenge

Area of Science:

• Computational chemistry
• Physical chemistry
• Drug discovery

Background:

• Accurate prediction of drug partition coefficients (log P) is crucial for drug discovery and development.
• Existing methods often struggle to precisely capture the complex energetic and entropic contributions to log P.
• Molecular dynamics (MD) simulations offer a powerful tool for studying molecular behavior in solution.

Purpose of the Study:

• To introduce and validate the energy-entropy multiscale cell correlation (EE-MCC) method for calculating toluene-water log P values.
• To assess the performance of EE-MCC against experimental data in the SAMPL9 physical properties challenge.
• To provide molecular-level understanding of the energy and entropy contributions to drug partitioning.

Main Methods:

• The energy-entropy multiscale cell correlation (EE-MCC) method was employed.
• EE-MCC calculates free energy, energy, and entropy from MD simulations of drug molecules in water and toluene solutions.
• Entropy is evaluated by partitioning the system into cells and energy wells at multiple length scales.

Main Results:

• EE-MCC achieved a mean average error of 0.82 and a standard error of the mean of 0.97 for log P values compared to experimental data.
• The calculated log P values were comparable to the best performing methods in the SAMPL9 challenge.
• Energy was identified as the primary contributor to log P, with less polar drugs exhibiting more favorable transfer energies.

Conclusions:

• The EE-MCC method provides a robust and accurate approach for predicting drug partition coefficients.
• The study elucidates the significant roles of both energy and entropy in drug partitioning behavior.
• Future software developments aim to address current limitations for a more comprehensive entropic calculation from MD simulations.