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Brozos C, Rittig JG, Bhattacharya S, Akanny E, Kohlmann C, Mitsos A. Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks. J Chem Theory Comput 2024; 20:5695-5707. [PMID: 38920084 DOI: 10.1021/acs.jctc.4c00314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in the industry. Recently, classical quantitative structure-property relationship (QSPR) and graph neural networks (GNNs), a deep learning technique, have been successfully applied to predict the CMC of surfactants at room temperature. However, these models have not yet considered the temperature dependence of the CMC, which is highly relevant to practical applications. We herein develop a GNN model for the temperature-dependent CMC prediction of surfactants. We collected about 1400 data points from public sources for all surfactant classes, i.e., ionic, nonionic, and zwitterionic, at multiple temperatures. We test the predictive quality of the model for the following scenarios: (i) when CMC data for surfactants are present in the training of the model in at least one different temperature and (ii) CMC data for surfactants are not present in the training, i.e., generalizing to unseen surfactants. In both test scenarios, our model exhibits a high predictive performance of R2 ≥ 0.95 on test data. We also find that the model performance varies with the surfactant class. Finally, we evaluate the model for sugar-based surfactants with complex molecular structures, as these represent a more sustainable alternative to synthetic surfactants and are therefore of great interest for future applications in the personal and home care industries.
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Affiliation(s)
- Christoforos Brozos
- BASF Personal Care and Nutrition GmbH, Henkelstrasse 67, 40589 Duesseldorf, Germany
- Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany
| | - Jan G Rittig
- Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany
| | - Sandip Bhattacharya
- BASF Personal Care and Nutrition GmbH, Henkelstrasse 67, 40589 Duesseldorf, Germany
| | - Elie Akanny
- BASF Personal Care and Nutrition GmbH, Henkelstrasse 67, 40589 Duesseldorf, Germany
| | - Christina Kohlmann
- BASF Personal Care and Nutrition GmbH, Henkelstrasse 67, 40589 Duesseldorf, Germany
| | - Alexander Mitsos
- Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany
- Institute of Energy and Climate Research IEK-10─Energy Systems Engineering, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- JARA Center for Simulation and Data Science (CSD), 52056 Aachen, Germany
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Boukelkal N, Rahal S, Rebhi R, Hamadache M. QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms. J Mol Graph Model 2024; 129:108757. [PMID: 38503002 DOI: 10.1016/j.jmgm.2024.108757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/07/2024] [Accepted: 03/10/2024] [Indexed: 03/21/2024]
Abstract
The determination of the critical micelle concentration (CMC) is a crucial factor when evaluating surfactants, making it an essential tool in studying the properties of surfactants in various industrial fields. In this present research, we assembled a comprehensive set of 593 different classes of surfactants including, anionic, cationic, nonionic, zwitterionic, and Gemini surfactants to establish a link between their molecular structure and the negative logarithmic value of critical micelle concentration (pCMC) utilizing quantitative structure-property relationship (QSPR) methodologies. Statistical analysis revealed that a set of 14 significant Mordred descriptors (SlogP, GATS6d, nAcid, GATS8dv, GATS4dv, PEOE_VSA11, GATS8d, ATS0p, GATS1d, MATS5p, GATS3d, NdssC, GATS6dv and EState_VSA4), along with temperature, served as appropriate inputs. Different machine learning methods, such as multiple linear regression (MLR), random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVM), were employed in this study to build QSPR models. According to the statistical coefficients of QSPR models, SVR with Dragonfly hyperparameter optimization (SVR-DA) was the most accurate in predicting pCMC values, achieving (R2 = 0.9740, Q2 = 0.9739, r‾m2 = 0.9627, and Δrm2 = 0.0244) for the entire dataset.
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Affiliation(s)
- Nada Boukelkal
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria.
| | - Soufiane Rahal
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria
| | - Redha Rebhi
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria
| | - Mabrouk Hamadache
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria
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Wu J, Yan F, Jia Q, Wang Q. QSPR for predicting the hydrophile-lipophile balance (HLB) of non-ionic surfactants. Colloids Surf A Physicochem Eng Asp 2021. [DOI: 10.1016/j.colsurfa.2020.125812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Liu T, Yan F, Jia Q, Wang Q. Norm index-based QSAR models for acute toxicity of organic compounds toward zebrafish embryo. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 203:110946. [PMID: 32888619 DOI: 10.1016/j.ecoenv.2020.110946] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/11/2020] [Accepted: 06/23/2020] [Indexed: 06/11/2023]
Abstract
Zebrafish embryos are highly sensitive to toxicant exposure and have been used to evaluate the potential eco-toxicity caused by organic pollutants in the aquatic environment. This study was to develop four quantitative structure-activity relationship (QSAR) models based on norm descriptors for acute toxicity of different exposure times toward zebrafish embryo of organic compounds with various structures. Norm descriptors were obtained by calculating the norm index of the atomic distribution matrix, which was composed of atomic spatial distribution and atomic properties. These norm index-based QSAR models presented satisfactory results with R2 of 0.8549, 0.9162, 0.8335 and 0.8119 for 48, 96, 120 and 132 h, respectively. Validation results including cross validation, external validation, Y-randomized test and applicability domain analysis indicated that the proposed models were stable, robust and reliable. Accordingly, these norm descriptors might be effective in predicting the acute toxicity of various organics to zebrafish embryos, which might be useful for evaluating the potential hazards of organic pollutants to aquatic environment.
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Affiliation(s)
- Ting Liu
- School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China
| | - Fangyou Yan
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China
| | - Qingzhu Jia
- School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China.
| | - Qiang Wang
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China
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Kashapov R, Gaynanova G, Gabdrakhmanov D, Kuznetsov D, Pavlov R, Petrov K, Zakharova L, Sinyashin O. Self-Assembly of Amphiphilic Compounds as a Versatile Tool for Construction of Nanoscale Drug Carriers. Int J Mol Sci 2020; 21:E6961. [PMID: 32971917 PMCID: PMC7555343 DOI: 10.3390/ijms21186961] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/18/2020] [Accepted: 09/19/2020] [Indexed: 12/12/2022] Open
Abstract
This review focuses on synthetic and natural amphiphilic systems prepared from straight-chain and macrocyclic compounds capable of self-assembly with the formation of nanoscale aggregates of different morphology and their application as drug carriers. Since numerous biological species (lipid membrane, bacterial cell wall, mucous membrane, corneal epithelium, biopolymers, e.g., proteins, nucleic acids) bear negatively charged fragments, much attention is paid to cationic carriers providing high affinity for encapsulated drugs to targeted cells. First part of the review is devoted to self-assembling and functional properties of surfactant systems, with special attention focusing on cationic amphiphiles, including those bearing natural or cleavable fragments. Further, lipid formulations, especially liposomes, are discussed in terms of their fabrication and application for intracellular drug delivery. This section highlights several features of these carriers, including noncovalent modification of lipid formulations by cationic surfactants, pH-responsive properties, endosomal escape, etc. Third part of the review deals with nanocarriers based on macrocyclic compounds, with such important characteristics as mucoadhesive properties emphasized. In this section, different combinations of cyclodextrin platform conjugated with polymers is considered as drug delivery systems with synergetic effect that improves solubility, targeting and biocompatibility of formulations.
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Affiliation(s)
- Ruslan Kashapov
- A.E. Arbuzov Institute of Organic and Physical Chemistry, FRC Kazan Scientific Center of RAS, Arbuzov street 8, Kazan 420088, Russia; (G.G.); (D.G.); (D.K.); (R.P.); (K.P.); (L.Z.); (O.S.)
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Shi Y, Yan F, Jia Q, Wang Q. Norm descriptors for predicting the hydrophile-lipophile balance (HLB) and critical micelle concentration (CMC) of anionic surfactants. Colloids Surf A Physicochem Eng Asp 2019. [DOI: 10.1016/j.colsurfa.2019.123967] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yan F, Liu T, Jia Q, Wang Q. Multiple toxicity endpoint-structure relationships for substituted phenols and anilines. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 663:560-567. [PMID: 30726764 DOI: 10.1016/j.scitotenv.2019.01.362] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 01/23/2019] [Accepted: 01/27/2019] [Indexed: 06/09/2023]
Abstract
Quantitative structure-toxicity relationship (QSTR) models with the same mathematical structure were proposed for predicting the multiple toxicity endpoints of substituted phenols and anilines towards Chlorella vulgaris (C. vulgaris) based on the norm indexes. Four aquatic toxicity endpoints including growth inhibition concentrations of IC50, IC20, LOEC and NOEC towards C. vulgaris were involved in the modeling work. The results indicated that the developed models could produce satisfactory predictive results for the four different toxicity endpoints with high squared correlation coefficients (R2). Leave-one-out cross validation, Y-randomized validation and application domain analysis demonstrated the accuracy, robustness and reliability of these models. Accordingly, the results obtained in this work suggested that it might be possible to develop QSTR models with the same mathematical structure for predicting multiple toxicity endpoints successfully via norm index descriptors.
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Affiliation(s)
- Fangyou Yan
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457 Tianjin, PR China
| | - Tingting Liu
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457 Tianjin, PR China
| | - Qingzhu Jia
- School of Marine and Environmental Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457 Tianjin, PR China
| | - Qiang Wang
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457 Tianjin, PR China.
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Jia Q, Zhao Y, Yan F, Wang Q. QSAR model for predicting the toxicity of organic compounds to fathead minnow. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:35420-35428. [PMID: 30350137 DOI: 10.1007/s11356-018-3434-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 10/09/2018] [Indexed: 06/08/2023]
Abstract
In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R2) and squared relation coefficient of the cross validation (Q2) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.
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Affiliation(s)
- Qingzhu Jia
- School of Marine and Environmental Science, Tianjin University of Science and Technology, 13 St. 29, TEDA, 300457, Tianjin, People's Republic of China
| | - Yunpeng Zhao
- School of Marine and Environmental Science, Tianjin University of Science and Technology, 13 St. 29, TEDA, 300457, Tianjin, People's Republic of China
| | - Fangyou Yan
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13 St. 29, TEDA, 300457, Tianjin, People's Republic of China
| | - Qiang Wang
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13 St. 29, TEDA, 300457, Tianjin, People's Republic of China.
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