离子液体中有机物pK<sub>a</sub>预测与泛化性能的机器学习模型研究

王晨阳; 肖建伟; 彭奎霖; 杨振

doi:10.11949/0438-1157.20260112

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离子液体中有机物pK_a预测与泛化性能的机器学习模型研究

研究论文 | 更新时间：2026-06-26

- 离子液体中有机物pK_a预测与泛化性能的机器学习模型研究
- A machine learning model for pK_a prediction and generalization of organic compounds in ionic liquids
- 化工学报 2026年
- 作者机构：
  
  江西师范大学化学工程学院，江西南昌 330022
- 作者简介：
  
  王晨阳（1999—），女，硕士研究生，201726701061@jxnu.edu.cn
  彭奎霖（1996—），男，博士，klpeng_1996@126.com
  杨振（1983—），男，博士，教授，yangzhen@jxnu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(22263006);江西省自然科学基金项目(20242BAB26028)
- DOI：10.11949/0438-1157.20260112
  中图分类号： O 645.1
- 收稿：2026-01-23，
  
  修回：2026-06-25，
  
  录用：2026-06-26，
- 稿件说明：
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王晨阳, 肖建伟, 彭奎霖, 等. 离子液体中有机物pK_a预测与泛化性能的机器学习模型研究[J/OL]. 化工学报, 2026. DOI: 10.11949/0438-1157.20260112.

WANG Chenyang, XIAO Jianwei, PENG Kuilin, et al. A machine learning model for pK_a prediction and generalization of organic compounds in ionic liquids[J/OL]. CIESC Journal, 2026. DOI: 10.11949/0438-1157.20260112.
王晨阳, 肖建伟, 彭奎霖, 等. 离子液体中有机物pK_a预测与泛化性能的机器学习模型研究[J/OL]. 化工学报, 2026. DOI: 10.11949/0438-1157.20260112. DOI：

WANG Chenyang, XIAO Jianwei, PENG Kuilin, et al. A machine learning model for pK_a prediction and generalization of organic compounds in ionic liquids[J/OL]. CIESC Journal, 2026. DOI: 10.11949/0438-1157.20260112. DOI：

摘要

有机化合物在离子液体中的解离常数（pK

）是关键物化参数，其实验测定成本高、周期长，而现有理论模型普遍存在跨溶剂泛化能力不足的问题。为此，本研究构建了两种机器学习预测框架，系统评估其性能边界与应用场景。首先，结合图神经网络（GNN）与机器学习算法（ML），利用GNN自动提取分子图特征，再输入至ML模型预测。在已知溶剂体系中，消息传递神经网络（MPNN）与梯度提升回归树（GBRT）组合模型表现最优，测试集R²达0.9822。进一步采用“留一溶剂”策略考察跨溶剂泛化能力，发现GNN+ML框架在该场景下效果不稳定。为此，本研究引入基于RDKit的分子描述符，结合机器学习构建跨溶剂自适应迁移学习模型。结果表明，基于RDKit描述符的模型在样本充足条件下能有效实现跨溶剂预测，其中原子对指纹（AP_FP）和RDKit指纹（RD_FP）与XGBoost或GBRT组合时，在多数测试集上R²超过0.95，展现出优异泛化性能。本研究为五种离子液体中pK

的高通量预测与溶剂设计提供了有效的计算工具。

Abstract

The dissociation constant (pK

) of organic compounds in ionic liquids (ILs) is a critical physicochemical property for understanding their behavior in these versatile and green solvent systems

yet experimental measurements remain costly and labor-intensive

and existing theoretical models often lack generalization across diverse solvent environments

particularly within the vast chemical space of ILs. To overcome these challenges

this study developed and systematically compared two distinct machine learning (ML) frameworks to evaluate their performance boundaries and applicable scenarios. First

a hybrid framewo

rk integrating Graph Neural Networks (GNNs)—specifically Message Passing Neural Networks (MPNN) and Graph Attention Networks (GAT)—with traditional ML algorithms was constructed to automatically extract topological and bond-level features from molecular graphs of both organic compounds and IL ions

which were then fed into six ML models including XGBoost

GBRT

Bagging

and ET; under an 80/20 train-test split within known IL systems

the MPNN+GBRT model achieved the best prediction performance with a test-set R

of 0.9822

MAE of 0.351

and RMSE of 0.524

demonstrating high in-distribution accuracy. To critically assess generalization to unseen ILs

a cross-solvent prediction task using a leave-one-solvent-out strategy revealed that the GNN+ML framework

represented by the optimal MPNN+GBRT model

exhibited unstable performance with the highest R

reaching only 0.9331 and showing strong dependence on training data ion structures. Given this limitation

an alternative approach employing RDKit-based molecular descriptors—including Atom Pair

Morgan

RDKit

and Torsion fingerprints—as feature inputs for ML models was pursued

and when evaluated on the same cross-solvent task

these descriptor-based models successfully achieved effective cross-solvent prediction under sufficient sample availability. In summary

this study underscores that the choice of feature representation is crucial for balancing predictive accuracy and generalization in IL pK

modeling

with the GNN+ML framework excelling in high-accuracy predictions within known chemical environments while the RDKit-descriptor-based ML approach offers a robust and generalizable solution for novel IL systems; this work not only provides two computational tools for high-throughput pK

prediction and solvent screening across five ILs but also delivers methodological insights into feature engineering for property prediction in tunable solvent systems

with future efforts directed toward expanding the IL dataset

ploring hybrid feature strategies

and improving model interpretability.

关键词

Keywords

references

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离子液体中有机物pKa预测与泛化性能的机器学习模型研究

A machine learning model for pKa prediction and generalization of organic compounds in ionic liquids

DOI：10.11949/0438-1157.20260112

摘要

Abstract

关键词

Keywords

references

离子液体中有机物pK_a预测与泛化性能的机器学习模型研究

A machine learning model for pK_a prediction and generalization of organic compounds in ionic liquids