These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
RJC_5721-2020.pdf (168.43 kB)

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

submitted on 16.01.2020, 03:02 and posted on 24.01.2020, 21:36 by Ely Setiawan, Mudasir Mudasir, Karna Wijaya

A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R2 = 0.95, q2 = 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R2 = 0.87, q2 = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.


Email Address of Submitting Author


Universitas Gadjah Mada



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest