Improving solvation energy predictions using the SMD solvation method and semiempirical electronic structure methods

Abstract

The PM6 implementation in the GAMESS program is extended to elements requiring -integrals and interfaced with the conducter-like polarized continuum model of solvation, including gradients. The accuracy of aqueous solvation energies computed using AM1, PM3, PM6, and DFT tight binding (DFTB) and the Solvation Model Density (SMD) continuum solvation model is tested using the Minnesota Solvation Database data set. The errors in SMD solvation energies predicted using Neglect of Diatomic Differential Overlap (NDDO)-based methods are considerably larger than when using density functional theory (DFT) and HF, with root mean square error (RMSE) values of 3.4-5.9 (neutrals) and 6-15 kcal/mol (ions) compared to 2.4 and ∼5 kcal/mol for HF/6-31G(d). For the NDDO-based methods, the errors are especially large for cations and considerably higher than the corresponding conductor-like screening model results, which suggests that the NDDO/SMD results can be improved by re-parameterizing the SMD parameters focusing on ions. We found that the best results are obtained by changing only the radii for hydrogen, carbon, oxygen, nitrogen, and sulfur, and this leads to RMSE values for PM3 (neutrals: 2.8/ions: ∼5 kcal/mol), PM6 (4.7/∼5 kcal/mol), and DFTB (3.9/∼5 kcal/mol) that are more comparable to HF/6-31G(d) (2.4/∼5 kcal/mol). Although the radii are optimized to reproduce aqueous solvation energies, they also lead more accurate predictions for other polar solvents such as dimethyl sulfoxide, acetonitrile, and methanol, while the improvements for non-polar solvents are negligible.

Deep Learning Model for Predicting Solvation Free Energies in Generic Organic Solvents

“…27 The general solubility equation (GSE) enables us to calculate solubilities from some empirical parameters, but it only provides solubilities for aqueous solutions. 28 Ab initio [1][2][3][4][5] or MD simulations 6-9 provide us with more concrete, accurate results and more in-depth knowledge about solvation mechanism, but they have practical limitations due to high usage of computational resources as mentioned before.…”

“…Scatter plot for true (x-axis) and ML predicted (y-axis) values of solvation energies in three different models: (a) BiLSTM, (b) BiGRU, and (c) without recurrent layers. All results are averaged over 9 independent 10-fold CV runs.Meanwhile, for studies which are not ML-based, there are several results from both classical and quantum-mechanical simulation studies that use the MNSOL as the reference data [2][3][4][5]7,9. A DFT study which introduced SM12 implicit solvation model calculated most of the solvation energies in the MNSOL 2.…”

Deep Learning Model for Predicting Solvation Free Energies in Generic Organic Solvents

“…The most common strategies to predict biological or physicochemical properties of chemical compounds are ab initio quantum mechanical approaches [1][2][3][4][5] like Hartree-Fock (HF) or density functional theory (DFT), and molecular dynamics (MD) simulation method based on classical Newtonian and statistical mechanics. [6][7][8][9] These methods with precisely defined theoretical backgrounds have been successfully used in calculating various features of chemical compounds.…”

“…27 The general solubility equation (GSE) enables us to calculate solubilities from some empirical parameters, but it only provides solubilities for aqueous solutions. 28 Ab initio [1][2][3][4][5] or MD simulations [6][7][8][9] provide us with more concrete, accurate results and more in-depth knowledge about solvation mechanism, but they have practical limitations due to high usage of computational resources as mentioned before.…”

“…[2][3][4][5]7,9 A Scatter plot for true (x-axis) and ML predicted (y-axis) values of solvation energies in three different models: (a) BiLSTM, (b) BiGRU, and (c) without recurrent layers. All results are averaged over 9 independent 10-fold CV runs.DFT study which introduced SM12 implicit solvation model calculated most of the solvation energies in the MNSOL.2 The researchers used several hybrid functionals and basis sets, calculated free energies of solvation of uncharged solutes for 374 aqueous solutions and 2129 non-aqueous solutions.…”