人工神经网络
水溶液
分子
机器学习
人工智能
计算机科学
价值(数学)
预测建模
预测值
生物系统
材料科学
算法
化学
物理化学
有机化学
内科学
生物
医学
作者
Qi Yang,Yao Li,Jin‐Dong Yang,Yidi Liu,Long Zhang,Sanzhong Luo,Jin‐Pei Cheng
标识
DOI:10.1002/anie.202008528
摘要
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.
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