Machine learning meets pKa [version 1; peer review: 2 approved]

Machine learning meets pKa [version 1; peer review: 2 approved]

Blog Article

We present a small molecule pKa prediction tool entirely written in Python.It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds.Different machine learning models were BRAIN FUNCTION tested and random forest performed best given a five-fold cross-validation (mean absolute Power Recliner error=0.682, root mean squared error=1.

032, correlation coefficient r2 =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.