Abstract
We present a small molecule pK a prediction tool entirely written in Python. It predicts the macroscopic pK a value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r 2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.
Highlights
The acid-base dissociation constant of a drug has a a far-reaching influence on pharmacokinetics by altering the solubility, membrane permeability and protein binding affinity of the drug
Due to the missing annotation, it remained unclear if different experimental methods were used or multiple measurements with the same experimental method have been performed
The developed model offers the possibility to predict pKa values for monoprotic molecules with good accuracy
Summary
The acid-base dissociation constant (pK ) of a drug has a a far-reaching influence on pharmacokinetics by altering the solubility, membrane permeability and protein binding affinity of the drug. Several publications summarize these findings in a very comprehensive manner[1,2,3,4,5,6,7]. Several (commercial and non-commercial) tools and approaches for small molecule pKa prediction are available: MoKa8 uses molecular interaction fields, whereas ACD/Labs Percepta Classic[9], Marvin[10] and Epik[11] make use of the HammetTaft equation. The publication by Williams et al.[15] makes use of a publicly available data set provided by the application DataWarrior[16] and provides a freely available pK prediction tool a called OPERA
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