Please Cite:
Qi Yang, Yao Li, Jin-Dong Yang, Yidi Liu, Long Zhang*, Sanzhong Luo*, Jin-Pei Cheng, Holistic Prediction of pKa in Diverse Solvents Based on Machine Learning Approach Intermediates. Angew. Chem. Int. Ed. 2020, 59, 19282-19291.

1. Abbreviations of solvents: H2O: Water; DMSO: Dimethyl sulfoxide; EtOH_50%: Ethanol:Water=50:50; AN: Acetonitrile; MeOH: Methanol.

2. Prediction methods: XGBoost with RMSE=1.79 and r2=0.918 (80:20 train test split).

3. Experimental data: experimental data comes from the sub-database of iBonD, for more details please click http://ibond.nankai.edu.cn.

4. Recommended pKa ranges: H2O: -2~16; DMSO: 5~35; EtOH_50%: 0~20; AN: 5~30; MeOH: 0~18.

5. Note: for special molecule's pKa which is out of the solvent leveling range, the experimental value is unreliable, so we exclude these values from training set. For these molecules, this model will return unreliable results.