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The embedded cluster reference interaction site model (EC-RISM) accurately predicted acidity constants (pKa) but showed limitations in predicting octanol-water partition coefficients (log P). Further optimization is needed for robust log P predictions in computational chemistry.

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Area of Science:

  • Computational Chemistry
  • Physical Organic Chemistry
  • Chemical Thermodynamics

Background:

  • The embedded cluster reference interaction site model (EC-RISM) combines quantum mechanics with 3D RISM theory for predicting species in solution.
  • This method was successfully applied to acidity constants (pKa) and octanol-water partition coefficients (log P) in previous SAMPL challenges (SAMPL6.1 and SAMPL6.2).

Purpose of the Study:

  • To apply the EC-RISM methodology to predict aqueous pKa and octanol-water log P values for the SAMPL7 physical property challenge.
  • To compute distribution coefficients (log D7.4) using predicted pKa and log P data.
  • To evaluate the performance and identify areas for improvement in the EC-RISM model for these physical properties.

Main Methods:

  • Utilized the embedded cluster reference interaction site model (EC-RISM) for quantum-mechanical calculations and 3D RISM theory.
  • Applied the methodology to predict pKa and log P values for the SAMPL7 challenge.
  • Calculated log D7.4 values by combining independent pKa and log P predictions.

Main Results:

  • Macroscopic pKa predictions showed excellent agreement with experimental data (RMSE = 0.72 pK units).
  • Log P predictions exhibited a higher RMSE (1.84), falling short of expectations from previous challenges.
  • Log D7.4 predictions were reasonable (RMSE = 1.69), benefiting from the combination of independent calculations.

Conclusions:

  • The EC-RISM model demonstrates strong predictive power for aqueous pKa values.
  • Further refinement of the EC-RISM model is necessary to improve the accuracy and robustness of octanol-water partition coefficient (log P) predictions.
  • Continued efforts are needed to align computational model descriptions with experimental conditions for enhanced predictive accuracy in physical property challenges.