Holistic Prediction of the p<i>K</i><sub>a</sub> in Diverse Solvents Based on a Machine‐Learning Approach
Qi Yang, Yao Li, Jin‐Dong Yang, Yidi Liu, Long Zhang, Sanzhong Luo, Jin‐Pei Cheng
Abstract
Abstract While many approaches to predict aqueous p K a values exist, the fast and accurate prediction of non‐aqueous p K a values is still challenging. Based on the iBonD experimental p K a database (39 solvents), a holistic p K a prediction model was established using machine learning. Structural and physical‐organic‐parameter‐based descriptors (SPOC) were introduced to represent the electronic and structural features of the molecules. The models trained with a neural network or the XGBoost algorithm showed the best prediction performance with a low MAE value of 0.87 p K a units. The approach allows a comprehensive mapping of all possible p K a correlations between different solvents and it was validated by predicting the aqueous p K a and micro‐p K a of pharmaceutical molecules and p K a values of organocatalysts in DMSO and MeCN with high accuracy. An online prediction platform was constructed based on the current model, which can provide p K a prediction for different types of X−H acidity in the most commonly used solvents.