Machine learning assisted QSPR model for prediction of ionic liquid's refractive index and viscosity: The effect of representations of ionic liquid and ensemble model development
机构：[1]Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China[2]Department of Pharmacy, Xi’an No.3 Hospital, The Affiliated Hospital of Northwest University, Xi’an, China[3]Department of Urology, Wuhan Third Hospital/Tongren Hospital of Wuhan University, Wuhan, China[4]Department of Pathophysiology, College of Basic Medicine, Guilin Medical University, Guilin, China[5]Department of Pharmacy, Xijing Hospital, Fourth Military Medical University, Xi’an, China
In this study, we used four ways to represent ionic liquids (ILs), namely, molecular fingerprint (MF), molecular descriptor (MD), the addition of MF (MF + MF) and the combination of MF and MD (MF_MD), to develop quantitative structure-property relationship (QSPR) models for predicting the refractive index and viscosity of ILs. Results showed that the predictive performance of QSPR models followed the order: MD < MF + MF < MF < MF_MD, indicating combining the chemical structure information and the physicochemical properties of ILs was beneficial to enhancing the predictive performance of the QSPR model. We also investigated the effect of the data splitting way on the predictive performance of the QSPR model, and the results showed that the group-based random splitting way was more reasonable than the random splitting way. The shapely additive explanation (SHAP) method was used to interpret MF_MD-based QSPR models. Results showed that different MDs play important role in prediction of refractive index and viscosity and the effects of conditions (temperature and/or pressure) were correctly identified. The QSPR model also correctly "learned" how MF affect the viscosity but wrongly "identified" how MF affect the refractive index. Finally, we developed the ensemble models by combining these single QSPR models to develop the final more accurate QSPR model. This study demonstrated that how to represent ILs plays important role in obtaining QSPR models with high predictive performance and developing the ensemble model was the possible efficient approach to further enhance the predictive performance of the QSPR model for ILs. (C) 2021 Elsevier B.V. All rights reserved.
基金：
Science Foundation of Shaanxi [2021KW-56]; Xi 'an Science and Technology Plan Project [201805103YX11SF37-17-7]; Scientific Research Project of Xi 'an Third Hospital [Y20202008]; Special Fundation for Guangxi Science and Technology for Base and Talents [2020AC19047]
第一作者机构：[1]Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
共同第一作者：
通讯作者：
通讯机构：[1]Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China[5]Department of Pharmacy, Xijing Hospital, Fourth Military Medical University, Xi’an, China[*1]Department of Pharmacy, The First Affilited Hospital of Zhengzhou University, Jianshedong Road No.1, Zhengzhou, China[*2]Department of Pharmacy, Xijing Hospital, Fourth Military Medical University, 127 Changle West Street, Xi’an, China
推荐引用方式(GB/T 7714)：
Sun Ya,Chen MinChun,Zhao Yongmei,et al.Machine learning assisted QSPR model for prediction of ionic liquid's refractive index and viscosity: The effect of representations of ionic liquid and ensemble model development[J].JOURNAL OF MOLECULAR LIQUIDS.2021,333:doi:10.1016/j.molliq.2021.115970.
APA：
Sun, Ya,Chen, MinChun,Zhao, Yongmei,Zhu, Zhenfeng,Xing, Han...&Ding, Yi.(2021).Machine learning assisted QSPR model for prediction of ionic liquid's refractive index and viscosity: The effect of representations of ionic liquid and ensemble model development.JOURNAL OF MOLECULAR LIQUIDS,333,
MLA：
Sun, Ya,et al."Machine learning assisted QSPR model for prediction of ionic liquid's refractive index and viscosity: The effect of representations of ionic liquid and ensemble model development".JOURNAL OF MOLECULAR LIQUIDS 333.(2021)
化学