Efficient Quantum-Chemical Calculations of Acid Dissociation Constants from Free-Energy Relationships.
Philipp PrachtStefan GrimmePublished in: The journal of physical chemistry. A (2021)
The calculation of acid dissociation constants (pKa) is an important task in computational chemistry and chemoinformatics. Theoretically and with minimal empiricism, this is possible from computed acid dissociation free energies via so-called linear free-energy relationships. In this study some modifications are introduced to the latter, providing a straightforward, broadly applicable protocol with an adjustable degree of sophistication for quantum chemistry-based calculations of pKa in water. It targets a wide pKa range (∼70 units) and medium-sized, flexible molecules. Herein, a focus is set on the recently published r2SCAN-3c and related efficient composite density functionals and the semiempirical GFN2-xTB method, including a newly introduced energy correction for heterolytic dissociation, both in combination with implicit solvation models. The performance is evaluated in comparison with experimental data, showing mean errors often smaller than a targeted 1 pKa unit accuracy. Larger deviations are observed only upon inclusion of challenging highly negative (<-5) or positive (>15) pKa values. Among all those tested, it is found that B97-3c is the best performing functional, although rather independently of the density functional theory (DFT) method used; low root-mean-square errors of 0.8-1.0 pKa units for typical drugs are obtained. For optimal performance, it is recommended to employ DFT functional specific free-energy relationship parameters. Additionally, a significant conformational dependence of the pKa values is revealed and quantified for some nonrigid drug molecules.
Keyphrases
- density functional theory
- molecular dynamics
- molecular dynamics simulations
- randomized controlled trial
- systematic review
- computed tomography
- electron transfer
- patient safety
- magnetic resonance
- monte carlo
- emergency department
- magnetic resonance imaging
- cancer therapy
- artificial intelligence
- machine learning
- molecular docking
- single cell
- quantum dots