Prediction of Mass Spectral Response Factors from Predicted Chemometric Data for Druglike Molecules

Cramer, C. J.; Johnson, J. L.; Kamel, A.

* J. Am. Soc. Mass Spectrom.*
**2017**, *28*, 285
(doi:10.1007/s13361-016-1536-4).

A method is developed for the prediction of mass spectral ion counts of
druglike molecules using in silico calculated chemometric data. Various
chemometric data, including polar and molecular surface areas, aqueous
solvation free energies, and gas-phase and aqueous proton affinities were
computed and a statistically significant relationship between measured mass
spectral ion counts and the combination of aqueous proton affinity and
total molecular surface area was identified. In particular, through
multilinear regression of ion counts on predicted chemometric data, we find
that
log_{10}(MS ion counts) = –4.824 +
*c*_{1} • PA +
*c*_{2} • SA where PA is the aqueous
proton affinity of the molecule computed at the
SMD(aq)/M06-L/MIDI!//M06-L/MIDI! level of electronic structure theory, SA
is the total surface area of the molecule in its conjugate base form, and
*c*_{1} and *c*_{2} have values of –3.912 x
10^{–2} mol kcal^{–1} and 3.682 x 10^{–3}
Å^{–2}.
On a 66 molecule training set, this regression exhibits a multiple *R*
value of 0.791 with *p* values for the intercept, *c*_{1},
and *c*_{2} of 1.4 x 10^{–3}, 4.3 x
10^{–10}, and 2.5 x 10^{–6}, respectively. Application of
this regression to an 11-molecule test set provides a good correlation of
prediction with
experiment (*R* = 0.905) albeit with a systematic underestimation of about
0.2 log units. This method may prove useful for semiquantitative analysis
of drug metabolites for which MS response factors or authentic standards
are not readily available.