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Benchmarking pKa Prediction and Algorithm Validation for Accurate pKa of Drugs Estimated from their Molecular Structures
Authors: Meloun Milan | Bordovská Sylva
Year: 2007
Type of publication: článek v odborném periodiku
Name of source: Analytical and Bioanalytical Chemistry
Publisher name: Springer
Page from-to: 1267-1281
Titles:
Language Name Abstract Keywords
cze Porovnání předpovědi pKa a validace algoritmu ke správnému odhadu pKa léčiv určeného z jejich molekulární struktury Porovnání předpovězených hodnot pKa a validace algoritmu ke správnému odhadu pKa léčiv určeného z jejich molekulární struktury programem REGDIA pro čtyři programy PALLAS, MARVIN, ACD/pK a SPARC je prézentováno. Cílem je odhalit vybočující hodnoty, které ničí systém vzájemného porovnání. Jsou uvedeny statistické testy a diagnostiky k posouzení a odhalení vybočujících předpovědí pK.
eng Benchmarking pKa Prediction and Algorithm Validation for Accurate pKa of Drugs Estimated from their Molecular Structures The REGDIA regression diagnostics algorithm in S-Plus is introduced to examine the accuracy of pKa predicted with four updated programs: PALLAS, MARVIN, ACD/pKa and SPARC. This report reviews the current status of computational tools for predicting the pKa of organic druglike compounds. Outliers in pKa relate to molecules which are poorly characterized by the pKa program concerned. The statistical detection of outliers can fail because of masking and swamping effects. The Williams graph was selected to give the most reliable detection of outliers. The six statistical characteristics, Fexp, R2, R2 P, MEP, AIC, and s(e) in pKa units, successfully examine the specimen of three datasets, also classifying four selected pKa prediction algorithms. The highest values of Fexp, R2, R2P and the lowest value of MEP and s(e) and the most negative AIC were found using the ACD/pKa algorithm of pKa prediction, so this algorithm achieves the best predictive power and the most accurate results. The proposed accuracy test of the REGDIA program can also be applied to test other predicted values, such as log P, log D, aqueous solubility or certain physicochemical properties of drug molecules. pKa prediction, pKa Accuracy, Dissociation constants, Outliers, Influential points, Residuals, Goodness-of-fit, Williams graph

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