Ivari Kaljurand

Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited availability of high-quality reference data poses a challenge in developing accurate, inexpensive computational prediction methods. In this study, we address both issues of data quality and availability. We present three databases and models related to ionic phenomena: 1 (1) 8,241 pK a datapoints across 8 solvents, (2) 5,536 gas-phase acidities from DLPNO- CCSD(T) QM calculations, and (3) 6,090 solvation free energies of anions across 8 solvents obtained from a thermodynamic cycle. We also report 6,088 solvation free energies of neutral conjugate solutes computed using the COSMO-RS method. The pKa data were obtained from the iBonD database, cleaned, and combined with a separate compilation of trustworthy reference pKa data. Gas-phase acidities were computed for most of the acids present in the pKa corpus. Leveraging these data, we compiled values for solvation free energies of anions. We then trained several graph neural network models, which can be used as an alternative to QM approaches to quickly estimate these properties. The pKa and gas-phase acidity models accept reaction SMILES strings of the acid dissociation as inputs, whereas the solvation energy model accepts the SMILES string of the anion. Our microscopic pKa model achieves good accuracy, with an overall test mean average error of 0.58 units on unseen solutes and 0.59 on the SAMPL7 challenge (the lowest error so far among multi-solvent models). Our gas-phase acidity model had mean absolute errors slightly above 2 kcal/mol when evaluated against experimental data. The anionic solvation free energy model had mean absolute errors of less than 3 kcal/mol in several test evaluations, comparable to (though less reliable than) several widely-used QM-based solvation models. The models and data are free and publicly available.

Acid dissociation constants () are widely measured and studied, most typically in water. Comparatively few datasets and models for non‐aqueous values exist. In this work, we demonstrate how the in one solvent can be accurately determined using reference data in another solvent, corrected by solvation energy calculations from the COSMO‐RS method. We benchmark this approach in 10 different solvents, and find that values calculated in six solvents deviate from experimental data on average by less than 1 unit. We observe comparable performance on a more diverse test set including amino acids and drug molecules, with higher error for large molecules. The model performance in four other solvents is worse, with one MAE exceeding 3 units; we discuss how such errors arise due to both model error and inconsistency in obtaining experimental data. Finally, we demonstrate how this technique can be used to estimate the proton transfer energy between different solvents, and use this to report a value of the proton's solvation energy in formamide, a quantity that does not have a consensus value in literature.

Acid dissociation constants (pKa) are widely measured and studied, most typically in water. Comparatively few datasets and models for non-aqueous pKa values exist. In this work, we demonstrate how the pKa in one solvent can be accurately determined using reference data in another solvent, corrected by solvation energy calculations from the COSMO-RS method. We benchmark this approach in ten different solvents, and find that pKa values calculated in six solvents deviate from experimental data on average by less than 1 pKa unit. We observe comparable performance on a more diverse test set including amino acids and drug molecules, with higher error for large molecules. The model performance in four other solvents is worse, with some MAEs exceeding 3 pKa units; we discuss how such errors arise due to both model error and inconsistency in obtaining experimental data. Finally, we demonstrate how this technique can be used to estimate the proton transfer energy between different solvents, and use this to report a value of the proton’s solvation energy in formamide, a quantity that does not have a consensus value in literature.

PK_aH values and θ_CPC or θ_H angles to fast track preliminary assessment of phosphanes.

The equilibrium acidity scale (pK_a scale) in acetonitrile has been supplemented by numerous new compounds and new ΔpK_a measurements. It now contains altogether 231 acids – over twice more than published previously – linked by 569 ΔpK_a measurements and spans between the pK_a values of hydrogen iodide (2.8) and indole (32.57), covering close to 30 orders of magnitude. Measurement results acquired over the last 15 years were added to the scale and new least‐squares treatment was carried out. The treatment yielded revised pK_a values for the compounds published previously, with the root mean square difference between revised and previous values 0.04, demonstrating very good stability of the scale. Correlation equations were developed for estimating pK_a values for the studied types of compounds in water, DMSO, DMF, and 1,2‐dichloroethane on the basis of pK_a values in acetonitrile. These equations enable predicting pK_a values with an average error around or less than 1 pK_a unit, which is a sufficient accuracy for many applications. The scale is expected to be a useful tool for the widest possible research areas in organic chemistry, electrochemical power sources, catalysis, etc.

Rationale: In recent years it has become increasingly evident that the previously reported experimental gas‐phase acidity ( GA ) values of several strong acids differ markedly from the corresponding high‐level computational values. In this work, the superacidic part of the current gas‐phase acidity scale was validated and extended. Methods: For that, the strongly acidic section of the gas‐phase acidity scale was remeasured using the equilibrium Fourier transform ion cyclotron resonance (FTICR‐MS) method, adding new compounds and introducing methodological changes. In particular, a novel approach for anchoring the scale was used – the results were anchored to the computational (W1BD) GA values of trifluoromethanesulfonic acid and bis(fluorosulfonyl)imide (291.3 and 286.2 kcal mol ⁻¹ , respectively). Results: The newly measured section consists of 20 gas‐phase superacids and its consistency standard deviation is 0.2 kcal mol ⁻¹ , indicating good consistency. In contrast to the previously reported experimental gas‐phase acidities for a number of important superacids, the current results are consistent with high‐level theoretical GA values. Structure‐acidity relationships based on the current results as well as available MeCN and DCE acidity data were described and explained. Conclusions: The introduced methodological innovations were found to be adequate and strong evidence is presented in support of the current GA values of the strong acids.

The basicities of simple organic bases – aliphatic and aromatic amines, amidines, phosphazenes, as well as saturated and unsaturated nitrogen heterocycles – are examined in acetonitrile, dimethyl sulfoxide, tetrahydrofuran, water and the gas phase. The basicities (pK_aH values) of conjugate acids of a large variety of bases in these media are presented and discussed. Equations employing easily usable structural descriptors have been derived for approximately converting basicities from acetonitrile to other solvents. Recommendations are given on their practical use and a number of pK_aH values that are experimentally unavailable are estimated from these relationships. An important part of the minireview is a large compilation of pK_aH and GB values of the compounds in solvents and the gas phase, respectively, as well as the revised basicity scale in acetonitrile, now containing more than 270 pK_aH values.