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pKa Prediction of Monoprotic Small Molecules the SMARTS Way

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Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109
* Corresponding author e-mail: [email protected]
Cite this: J. Chem. Inf. Model. 2008, 48, 10, 2042–2053
Publication Date (Web):October 1, 2008
https://doi.org/10.1021/ci8001815
Copyright © 2008 American Chemical Society
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    Abstract

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    Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pKa for small molecules. Using curated data from the Beilstein Database and Lange’s Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pKa for monoprotic compounds with R2 of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q2 of 0.91 and root mean squared error of 0.80.

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    Decision tree including predictions and ranges for each node and SMILES for all compounds and the predicted pKa values for all test compounds from SMARTS pKa, SPARC, MARVIN, Advanced Chemistry Development (ACD)/Labs Online v. 8.03, and ADME Boxes. This material is available free of charge via the Internet at http://pubs.acs.org.

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    26. A. P. Harding, P. L. A. Popelier. pKa Prediction from an ab initio bond length: part 2—phenols. Physical Chemistry Chemical Physics 2011, 13 (23) , 11264. https://doi.org/10.1039/c1cp20379g
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