Ligand Entropy in Gas-Phase, Upon Solvation and Protein Complexation. Fast Estimation with Quasi-Newton Hessian

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Biophysics

Background:

• Entropy estimation is crucial for molecular simulations.
• Accurate entropy calculation is computationally demanding.
• Understanding solvation entropy is key in drug discovery.

Purpose of the Study:

• To develop a rapid entropy estimation method for small molecules.
• To evaluate the Hessian matrix for vibrational entropy prediction.
• To compare analytical and rigorous solvation models.

Main Methods:

• Utilized quasi-Newton optimization to build the Hessian matrix.
• Employed classical molecular potentials for molecular geometry optimization.
• Applied a simple analytical solvation model and scaled particle theory.

Main Results:

• The Hessian matrix accurately predicts vibrational entropies in vacuum, solution, and protein environments.
• An analytical solvation model achieved comparable accuracy to Poisson-based models.
• Scaled particle theory improved hydrophobic solvation entropy estimation.

Conclusions:

• A computationally efficient method for entropy estimation is presented.
• Analytical solvation models offer a viable alternative for solution-phase entropy calculations.
• The proposed method facilitates faster and more accurate molecular simulations.