Comparative Evaluation of in Silico pK_a Prediction Tools on the Gold Standard Dataset

Abstract

Abstract The predictive performance of five different p K a prediction tools (ACDpKa, Epik, Marvin pKa, Pallas pKa, and VCCpKa) was investigated on the 248‐membered Gold Standard dataset. We found VCC as the most predictive, high throughput p K a predictor. However since VCC calculates p K a for the most acidic or basic group only we concluded that ACD and Marvin are in fact the method of choice for medicinal chemistry applications. Analyzing the common outliers we identified guanidines, enolic hydroxyl groups and weak acidic NHs as most problematic moieties from prediction point of view. Our results obtained on the high quality, homogenous Gold Standard dataset could be useful for end‐users selecting a suitable solution for p K a prediction.