Abstract In this paper, group contribution (GC) property models for the estimation of acid dissociation constants (Ka) of organic compounds are presented. Three GC models are developed to predict the negative logarithm of the acid dissociation constant pKa: (a) a linear GC model for amino acids using 180 data-points with average absolute error of 0.23; (b) a non-linear GC model for organic compounds using 1622 data-points with average absolute error of 1.18; (c) an artificial neural network (ANN) based GC model for the organic compounds with average absolute error of 0.17. For each of the developed model, uncertainty estimates for the predicted pKa values are also provided. The model details, regressed parameters and application examples are highlighted.

中文翻译：

---

使用基团贡献法预测有机化合物的酸解离常数

摘要 本文提出了用于估计有机化合物酸解离常数 (Ka) 的基团贡献 (GC) 特性模型。开发了三种 GC 模型来预测酸解离常数 pKa 的负对数： (a) 氨基酸的线性 GC 模型，使用 180 个数据点，平均绝对误差为 0.23；(b) 有机化合物的非线性 GC 模型，使用 1622 个数据点，平均绝对误差为 1.18；(c) 基于人工神经网络 (ANN) 的有机化合物 GC 模型，平均绝对误差为 0.17。对于每个开发的模型，还提供了预测 pKa 值的不确定性估计。突出显示了模型详细信息、回归参数和应用示例。