ACADSTAFF UGM

CREATION
Title : QSPR MODELS FOR PREDICTING CRITICAL MICELLE CONCENTRATION OFGEMINI CATIONIC SURFACTANTS COMBINING MACHINE-LEARNINGMETHODS AND MOLECULAR DESCRIPTORS
Author :

ELY SETIAWAN (1) Prof. Drs. Mudasir, M.Eng., Ph.D. (2) Prof. Dr. rer. nat. Karna Wijaya, M.Eng. (3)
Date : 1 2020
Keyword : critical micelle concentration, gemini cationic surfactant, machine learning,OCHEM, QSPR critical micelle concentration, gemini cationic surfactant, machine learning,OCHEM, QSPR
Abstract : A data set of 231 diverse gemini cationic surfactants has been developed to correlate thelogarithm of critical micelle concentration (cmc) with the molecular structure using aquantitative structure-property relationship (QSPR) methods. The QSPR models weredeveloped using the Online CHEmical Modeling environment (OCHEM). It provides severalmachine learning methods and molecular descriptors sets as a tool to build QSPR models.Molecular descriptors were calculated by eight different software packages including Dragonv6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS,and PyDescriptor. A total of 64 QSPR models were generated, and one consensus modeldeveloped by using a simple average of 13 top-ranked individual models. Based on thestatistical coefficient of QSPR models, a consensus model was the best QSPR models. Themodel provided the highest R2 = 0.95, q2 = 0.95, RMSE = 0.16 and MAE = 0.11 for trainingset, and R2 = 0.87, q2 = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freelyavailable at https://ochem.eu/model/8425670 and can be used for estimation of cmc of newgemini cationic surfactants compound at the early steps of gemini cationic surfactantsdevelopment
Group of Knowledge : Kimia
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 QSPR_Models_for_Predicting_Critical_Micelle_Concentration_of_Gemini_Cationic_Surfactants_Combining_Machine-Learning_Meth_v1.pdf
Document Type : Bukti Published
Bukti Published View
2 Ely S-ChemArchaive_2020.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
3 Cek similirarity_MDS_General.pdf
Document Type : [PAK] Cek Similarity
[PAK] Cek Similarity View
4 Bukti korespondensi_MDS_Umum.pdf
Document Type : [PAK] Bukti Korespondensi Penulis
[PAK] Bukti Korespondensi Penulis View