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

Cramer, Christopher J, Johnson, Joshua and Kamel, Amin (2017) Prediction of Mass Spectral Response Factors from Predicted Chemometric Data for Druglike Molecules. Journal of the American Society for Mass Spectrometry, 28 (2). pp. 278-285. ISSN 1879-1123


A method is developed for the prediction of mass spectral ion counts of drug-like 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 log10(MS ion counts) = –4.824 + c1•PA + c2•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 c1 and c2 have values of –3.912 × 10–2 mol kcal–1 and 3.682 × 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, c1, and c2 of 1.4 × 10–3, 4.3 × 10–10, and 2.5 × 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. [Figure not available: see fulltext.]

Item Type: Article
Keywords: Aqueous proton affinity Chemometric data Mass spectral ion counts of drug-like molecules Total surface area
Date Deposited: 22 Nov 2017 00:45
Last Modified: 25 Jan 2019 00:45


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