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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. 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Holistic Prediction of the p<i>K</i><sub>a</sub> in Diverse Solvents Based on a Machine‐Learning Approach 论文

Holistic Prediction of the p<i>K</i><sub>a</sub> in Diverse Solvents Based on a Machine‐Learning Approach · 相关技术

相关技术