I screwed up some of the calculations describes in this post so I am starting fresh.

In an earlier study we showed that pKa values could be computed fairly accurately using PM6-based methods using isodesmic reactions and appropriate reference compounds. Amines were especially challenging. The study used small molecules. The next question, which I address here, is how this approach performs for actual drug molecules containing amine functional groups.

I tool about 50 drug-like molecules with experimentally measured amine pKa values from Table 3 in the paper by Eckert and Klamt. I moved some of the smaller molecules such as 2-methylbenzylamine, since they would differ very little from the corresponding reference molecules. The reference molecules are chosen to match the chemical environment of the nitrogen within a 2 bond radius as much as practically possible and I try match the ring-size if the nitrogen sits in a ring. I end up with 35 reference molecules. I compute most of the reference pKa values using the ACE JChem pKa predictor.

Many of the molecules contain more than one ionizable group. Only the pKa values of the amine indicated in Eckert and Klamt's Table 3 are computed and the protonation states are prepared according standard pKa values. For example, for phenylalanine the carboxyl group is deprotonated because the "standard" pKa values of a carboxyl group (e.g. in acetic acid) is lower than the standard pKa values of a primary amine (e.g. ethylamine). Notice that the cyanoguanidine group in cimetidine has a pKa value of about 0 and is therefore deprotonated when the imidazole group titrates. Eckert and Klamt characterised the histamine pKa value of 9.7 as an amidine pKa and the thenyldiamine pKa as a pyridine pKa. This is corrected to a primary amine and tertiary amine, respectively. Also, the experimental pKa values of morphine and niacin are changed to 8.2 and 4.2, respectively, while the remaining experimental pKa values are taken from Eckert and Klamt.

I test the following methods: PM6-DH+, PM6, PM3 and AM1 with the COSMO solvation method using MOPAC, and PM3, AM1, and DFTB3 with the SMD solvation method in GAMESS. Jimmy's getting quite close to finishing the PM6 implementation for heavy elements in GAMESS, but he still needs to interface it with PCM, so PM6/SMD calculations are not yet possible. I use RDKit to generate 20 starting geometries for each protonation state and I optimise with the solvation method turned on. I don't include RRHO free energy contributions and I use only the lowest free energy structure for the pKa calculations.

The results are shown in the figure above (based on "Table 1 soln"). The negative outlier seen for the COSMO-based method is cefradoxil and is due to proton transfer in the zwitterionic protonation state. Cefadroxil is also the negative outlier for DFTB3/SMD although the proton doesn't transfer. Proton transfer in zwitterions is also a common problem for DFT/continuum calculations and is due to deficiencies in the continuum solvent method, not the electronic structure method. The good performance observed for PM3/SMD is thus due to fortuitous cancellation of error. For the three other zwitterions among the molecules, niacin, phenylalanine, and tryptophan, no proton transfer is observed and the error is relatively small.

The AM1- and PM3-based methods perform best with RMSE values of 1.4-1.6 ± 0.3-0.4, that are statistically identical.* The null model has an RMSE of 1.8 ± 0.4 which, given the error bars, are statistically no worse than the AM1- and PM3-based method.

If the cefadroxil outlier is removed, the RMSE values for PM3/COSMO and AM1/COSMO drop to 1.0 ± 0.2 and 1.1 ± 0.3, while PM3/SMD and AM1/SMD are remain at 1.5 ± 0.4 and 1.6 ± 0.4. So for this subset, the COSMO-based predictions can be said to outperform the SMD-based predictions, as well as the null model.

One of the main uses of pKa values is the prediction of the correct protonation state at physiological pH (7.4), i.e. is the predicted pKa above or below 7.4? Here PM3/COSMO is performs best by getting it right 94% of the time, compared to 91%, 77%, and 91% for AM1/COSMO, PM3/SMD, and the null model.

In an earlier study we showed that pKa values could be computed fairly accurately using PM6-based methods using isodesmic reactions and appropriate reference compounds. Amines were especially challenging. The study used small molecules. The next question, which I address here, is how this approach performs for actual drug molecules containing amine functional groups.

*These are preliminary results and may contain errors.*

**The Molecules**I tool about 50 drug-like molecules with experimentally measured amine pKa values from Table 3 in the paper by Eckert and Klamt. I moved some of the smaller molecules such as 2-methylbenzylamine, since they would differ very little from the corresponding reference molecules. The reference molecules are chosen to match the chemical environment of the nitrogen within a 2 bond radius as much as practically possible and I try match the ring-size if the nitrogen sits in a ring. I end up with 35 reference molecules. I compute most of the reference pKa values using the ACE JChem pKa predictor.

Many of the molecules contain more than one ionizable group. Only the pKa values of the amine indicated in Eckert and Klamt's Table 3 are computed and the protonation states are prepared according standard pKa values. For example, for phenylalanine the carboxyl group is deprotonated because the "standard" pKa values of a carboxyl group (e.g. in acetic acid) is lower than the standard pKa values of a primary amine (e.g. ethylamine). Notice that the cyanoguanidine group in cimetidine has a pKa value of about 0 and is therefore deprotonated when the imidazole group titrates. Eckert and Klamt characterised the histamine pKa value of 9.7 as an amidine pKa and the thenyldiamine pKa as a pyridine pKa. This is corrected to a primary amine and tertiary amine, respectively. Also, the experimental pKa values of morphine and niacin are changed to 8.2 and 4.2, respectively, while the remaining experimental pKa values are taken from Eckert and Klamt.

**The Methods**I test the following methods: PM6-DH+, PM6, PM3 and AM1 with the COSMO solvation method using MOPAC, and PM3, AM1, and DFTB3 with the SMD solvation method in GAMESS. Jimmy's getting quite close to finishing the PM6 implementation for heavy elements in GAMESS, but he still needs to interface it with PCM, so PM6/SMD calculations are not yet possible. I use RDKit to generate 20 starting geometries for each protonation state and I optimise with the solvation method turned on. I don't include RRHO free energy contributions and I use only the lowest free energy structure for the pKa calculations.

**The Results**The results are shown in the figure above (based on "Table 1 soln"). The negative outlier seen for the COSMO-based method is cefradoxil and is due to proton transfer in the zwitterionic protonation state. Cefadroxil is also the negative outlier for DFTB3/SMD although the proton doesn't transfer. Proton transfer in zwitterions is also a common problem for DFT/continuum calculations and is due to deficiencies in the continuum solvent method, not the electronic structure method. The good performance observed for PM3/SMD is thus due to fortuitous cancellation of error. For the three other zwitterions among the molecules, niacin, phenylalanine, and tryptophan, no proton transfer is observed and the error is relatively small.

The AM1- and PM3-based methods perform best with RMSE values of 1.4-1.6 ± 0.3-0.4, that are statistically identical.* The null model has an RMSE of 1.8 ± 0.4 which, given the error bars, are statistically no worse than the AM1- and PM3-based method.

If the cefadroxil outlier is removed, the RMSE values for PM3/COSMO and AM1/COSMO drop to 1.0 ± 0.2 and 1.1 ± 0.3, while PM3/SMD and AM1/SMD are remain at 1.5 ± 0.4 and 1.6 ± 0.4. So for this subset, the COSMO-based predictions can be said to outperform the SMD-based predictions, as well as the null model.

One of the main uses of pKa values is the prediction of the correct protonation state at physiological pH (7.4), i.e. is the predicted pKa above or below 7.4? Here PM3/COSMO is performs best by getting it right 94% of the time, compared to 91%, 77%, and 91% for AM1/COSMO, PM3/SMD, and the null model.

**Summary and Outlook**

Overall the best method for pKa prediction of drug-like molecules is either PM3/SMD, AM1/SMD, or the null model with RMSE values of 1.5 ± 0.4, 1.6 ± 0.4, and 1.8 ± 0.4. The corresponding RMSE values for PM3/COSMO and AM1/COSMO are very similar, but in general they can fail for certain types of zwitterions.

For a set of molecules that does not include such zwitterions then the best method is either PM3/COSMO and AM1/COSMO which deliver RMSE values of 1.0 ± 0.2 and 1.1 ± 0.3, respectively. In this case, using gas phase geometries lead to slightly larger , but statistically identical, RMSE values of 1.4 ± 0.3 and 1.3 ± 0.3, respectively.

In this study I made sure that the suitable reference molecules where available for all molecules. This will be difficult in the general case and it will be interesting to see how the accuracy is for these molecules. Cases where no good reference molecule can be found can be flagged based on similarity scores.

I will now start working on a manuscript draft (you can follow along here if you're interested)

*The RMSE uncertainties are computed using equation 14 in this paper and the two methods are taken to be statistically different if their difference in RMSE is larger than the composite error described on page 105 of this paper.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.

For a set of molecules that does not include such zwitterions then the best method is either PM3/COSMO and AM1/COSMO which deliver RMSE values of 1.0 ± 0.2 and 1.1 ± 0.3, respectively. In this case, using gas phase geometries lead to slightly larger , but statistically identical, RMSE values of 1.4 ± 0.3 and 1.3 ± 0.3, respectively.

In this study I made sure that the suitable reference molecules where available for all molecules. This will be difficult in the general case and it will be interesting to see how the accuracy is for these molecules. Cases where no good reference molecule can be found can be flagged based on similarity scores.

I will now start working on a manuscript draft (you can follow along here if you're interested)

*The RMSE uncertainties are computed using equation 14 in this paper and the two methods are taken to be statistically different if their difference in RMSE is larger than the composite error described on page 105 of this paper.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.