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Updated: Jun 15, 2026

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Published on: April 3, 2026
Evaluation of pKa estimation methods on 211 druglike compounds
John Manchester1, Grant Walkup, Olga Rivin
1Infection Discovery, AstraZeneca R&D Boston, 35 Gatehouse Drive, Waltham, Massachusetts 02451, USA. john.manchester@astrazeneca.com
Accurate pK(a) prediction for druglike compounds is crucial. Capillary electrophoresis determined experimental pK(a) values, finding ACD, Marvin, and MoKa performed similarly, outperforming Epik and Pipeline Pilot.
Area of Science:
- Medicinal Chemistry
- Analytical Chemistry
- Computational Chemistry
Background:
- Accurate prediction of pK(a) values is essential for drug discovery and development.
- Experimental determination and computational estimation of pK(a) are key processes.
Purpose of the Study:
- To experimentally determine pK(a) values for 211 druglike compounds.
- To compare the accuracy of five commercial pK(a) estimation software packages.
- To evaluate the impact of different prediction algorithms and versions on accuracy.
Main Methods:
- Experimental determination of pK(a) using capillary electrophoresis (CE) and ultraviolet spectroscopy.
- Application of a novel fitting algorithm for data analysis.
- Comparison of experimental data with predictions from ACDLabs/pK(a), Marvin, MoKa, Epik, and Pipeline Pilot.
Main Results:
- ACDLabs/pK(a), Marvin, and MoKa showed statistically indistinguishable accuracy, with a root-mean-squared error of approximately 1 pK(a) unit.
- MoKa was faster than ACDLabs/pK(a).
- Pipeline Pilot and Epik demonstrated significantly lower agreement with experimental values.
- ACD v10 outperformed ACD v12 for some compounds.
- Apparent pK(a) predictions were more accurate than microscopic pK(a) predictions.
Conclusions:
- Several commercial pK(a) estimation tools offer comparable accuracy for druglike compounds.
- Software selection and version can impact prediction reliability.
- Apparent pK(a) is a more reliable metric for experimental comparison than microscopic pK(a).
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