AIggregate: A Machine Learning Approach for Classifying Micelle Shape
Authors: 
Alkiviadis Mertzios, Konstantinos Papavasileiou, Loukas Peristeras, 
George GiannakopoulosAuthors Info & Claims
Article No.: 58, Pages 1 - 7
Abstract
In this work we develop AIggregate, a machine learning tool for classifying the shapes of micelles, which are clusters of surfactant molecules self assembled in aqueous solutions due to their unique amphiphilic character. For the majority of these systems, as the concentration of the surfactant increases, the micellar shape changes from spherical to elongated at a specific value defining the second critical micellar concentration (CMC). Known methods aiming to classify the micelles’ shape and to specify the second CMC with molecular modeling use heuristic approaches. These are based on shape parameters like asphericity, acylindricity, and anisotropicity. We expand upon this approach by applying machine learning and deep learning architectures to classify the shape of molecular assemblies. To achieve our goal, AIggregate uses both a point cloud representation of the micelle, where each atom or group of atoms constitutes a point in the cloud, as well as shape parameters of the assembly. We found that these methods significantly improve classification accuracy over the heuristic approach, with the deep learning, point-cloud-based method offering the maximum efficiency among the examined methods.
References
[1]
AIggregate. 2022. AIggregate: A Machine Learning Approach for Classifying Micelle Shape. https://github.com/alkiviadis-mertzios/AIggregate
[2]
Stefanos D. Anogiannakis, Panagiotis C. Petris, and Doros N. Theodorou. 2020. Promising Route for the Development of a Computational Framework for Self-Assembly and Phase Behavior Prediction of Ionic Surfactants Using MARTINI. Pure and Applied Chemistry 124, 3 (2020), 556–567. https://doi.org/10.1021/acs.jpcb.9b09915 arXiv:https://doi.org/10.1021/acs.jpcb.9b09915
[3]
Iro Armeni, Ozan Sener, Amir Roshan Zamir, Helen Jiang, Ioannis K. Brilakis, Martin Fischer, and Silvio Savarese. 2016. 3D Semantic Parsing of Large-Scale Indoor Spaces. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1534–1543. https://doi.org/10.1109/CVPR.2016.170
[4]
R. R. Balmbra, J. S. Clunie, and J. F. Goodman. 1969. Cubic Mesomorphic Phases. Pure and Applied Chemistry 222, 5199 (June 1969), 1159–1160. https://doi.org/10.1038/2221159a0
[5]
Magnus L. Bergström. 2016. Second CMC in surfactant micellar systems. Pure and Applied Chemistry 22 (April 2016), 46–50. https://doi.org/10.1016/j.cocis.2016.02.008
[6]
Magnus L. Bergström. 2018. A theoretical investigation of the influence of the second critical micelle concentration on the solubilization capacity of surfactant micelles. Pure and Applied Chemistry 8, 5 (May 2018), 055136. https://doi.org/10.1063/1.5027062
[7]
David Chandler. 2005. Interfaces and the Driving Force of Hydrophobic Assembly. Pure and Applied Chemistry 437 (10 2005), 640–7. https://doi.org/10.1038/nature04162
[8]
Zhengfei Chen, Tamar L. Greaves, Celesta Fong, Rachel A. Caruso, and Calum J. Drummond. 2012. Lyotropic liquid crystalline phase behaviour in amphiphile–protic ionic liquid systems. Pure and Applied Chemistry 14, 11 (2012), 3825. https://doi.org/10.1039/c2cp23698b
[9]
Szu-Chia Chien, Germán Pérez-Sánchez, José R. B. Gomes, M. Natália D. S. Cordeiro, Miguel Jorge, Scott M. Auerbach, and Peter A. Monson. 2017. Molecular Simulations of the Synthesis of Periodic Mesoporous Silica Phases at High Surfactant Concentrations. Pure and Applied Chemistry 121, 8 (2017), 4564–4575. https://doi.org/10.1021/acs.jpcc.6b09429 arXiv:https://doi.org/10.1021/acs.jpcc.6b09429
[10]
Douglas H. Everett. 1972. Manual of Symbols and Terminology for Physicochemical Quantities and Units, Appendix II: Definitions, Terminology and Symbols in Colloid and Surface Chemistry. Pure and Applied Chemistry 31, 4 (Jan. 1972), 577–638. https://doi.org/10.1351/pac197231040577
[11]
Simon Haykin. 1999. Neural Networks: A Comprehensive Foundation. Prentice Hall.
[12]
Ulf Henriksson, Eunice S. Blackmore, Gordon J. T. Tiddy, and Olle Soederman. 1992. Intermediate liquid crystalline phases in the binary system C16TACl-H2O: an NMR and low-angle x-ray diffraction study. Pure and Applied Chemistry 96, 9 (April 1992), 3894–3902. https://doi.org/10.1021/j100188a059
[13]
Vitaly Kocherbitov. 2015. Molecular dynamics simulations of liquid crystalline phases of dodecyltrimethylammonium chloride. Pure and Applied Chemistry 210 (2015), 74–81. https://doi.org/10.1016/j.molliq.2015.06.008 Surfactants in Solution. Selected Papers presented at the 20th International Symposium on Surfactants in Solution, 22 - 27 June 2014.
[14]
Jean-Bernard Maillet, Véronique Lachet, and Peter V. Coveney. 1999. Large scale molecular dynamics simulation of self-assembly processes in short and long chain cationic surfactants. Pure and Applied Chemistry 1 (1999), 5277–5290. Issue 23. https://doi.org/10.1039/A905216J
[15]
Angelos Malliaris, Jacques Le Moigne, Jean Sturm, and Raoul Zana. 1985. Temperature dependence of the micelle aggregation number and rate of intramicellar excimer formation in aqueous surfactant solutions. Pure and Applied Chemistry 89, 12 (June 1985), 2709–2713. https://doi.org/10.1021/j100258a054
[16]
Paul J. Missel, Norman A. Mazer, Martin C. Carey, and George B. Benedek. 1989. Influence of alkali-metal counterion identity on the sphere-to-rod transition in alkyl sulfate micelles. Pure and Applied Chemistry 93, 26 (Dec. 1989), 8354–8366. https://doi.org/10.1021/j100363a014
[17]
Lydia Paccamiccio, Michela Pisani, Francesco Spinozzi, Claudio Ferrero, Stephanie Finet, and Paolo Mariani. 2006. Pressure Effects on Lipidic Direct Phases: The Dodecyl Trimethyl Ammonium Chloride-Water System. Pure and Applied Chemistry 110, 25 (June 2006), 12410–12418. https://doi.org/10.1021/jp054467d
[18]
E. M. Piotrovskaya, A. A. Vanin, and N. A. Smirnova. 2006. Molecular dynamics simulation of micellar aggregates in aqueous solution of hexadecyl trimethylammonium chloride with different additives. Pure and Applied Chemistry 104, 22-24 (2006), 3645–3651. https://doi.org/10.1080/00268970601014807 arXiv:https://doi.org/10.1080/00268970601014807
[19]
PointNet. 2016. Deep Learning on Point Sets for 3D Classification and Segmentation. https://github.com/charlesq34/pointnet
[20]
pysimpp. 2022. Post-process functionality and utilities for molecular simulations. https://github.com/loudove/pysimpp
[21]
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2016. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Pure and Applied Chemistry abs/1612.00593 (2016). arXiv:1612.00593http://arxiv.org/abs/1612.00593
[22]
Doros N. Theodorou and Ulrich Suter. 1985. Shape of unperturbed linear polymers: polypropylene. Pure and Applied Chemistry 18 (1985), 1206–1214.
[23]
Dietterich G. Thomas. 1998. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Pure and Applied Chemistry 10, 7 (1998), 1895–1923. https://doi.org/10.1162/089976698300017197
[24]
Flora D. Tsourtou, Stavros D. Peroukidis, and Loukas D. Peristeras. 2022. The phase behaviour of cetyltrimethylammonium chloride surfactant aqueous solutions at high concentrations: an all-atom molecular dynamics simulation study. Pure and Applied Chemistry 18 (2022), 1371–1384. Issue 7. https://doi.org/10.1039/D1SM01639C
[25]
A. A. Vanin, E. M. Piotrovskaya, and N. A. Smirnova. 2007. A molecular dynamics simulation of micellar aggregates in aqueous solutions of hexadecyltrimethylammonium chloride with admixtures of low-molecular-weight substances. Pure and Applied Chemistry 81, 8 (Aug. 2007), 1256–1262. https://doi.org/10.1134/S0036024407080146
[26]
Zuowei Wang and Ronald G. Larson. 2009. Molecular Dynamics Simulations of Threadlike Cetyltrimethylammonium Chloride Micelles: Effects of Sodium Chloride and Sodium Salicylate Salts. Pure and Applied Chemistry 113, 42 (2009), 13697–13710. https://doi.org/10.1021/jp901576e arXiv:https://doi.org/10.1021/jp901576ePMID: 19476369.
[27]
Zhirong Wu, Shuran Song, Aditya Khosla, Xiaoou Tang, and Jianxiong Xiao. 2014. 3D ShapeNets for 2.5D Object Recognition and Next-Best-View Prediction. Pure and Applied Chemistry abs/1406.5670 (2014). arXiv:1406.5670http://arxiv.org/abs/1406.5670
[28]
Dmitry S. Yakovlev and Edo S. Boek. 2007. Molecular Dynamics Simulations of Mixed Cationic/Anionic Wormlike Micelles. Pure and Applied Chemistry 23, 12 (2007), 6588–6597. https://doi.org/10.1021/la063268y arXiv:https://doi.org/10.1021/la063268yPMID: 17477551.
[29]
Li Yi, Leonidas Guibas, Vladimir Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, ARCewu Lu, Qixing Huang, and Alla Sheffer. 2016. A scalable active framework for region annotation in 3D shape collections. Pure and Applied Chemistry 35 (11 2016), 1–12. https://doi.org/10.1145/2980179.2980238
Index Terms
- AIggregate: A Machine Learning Approach for Classifying Micelle Shape
Recommendations
In-silico approach to investigate death domains associated with nano-particle-mediated cellular responses
Graphical abstractDisplay Omitted
Highlights- Death Domain superfamily of nanoparticle mediated inflammation-apoptosis studied.
AbstractThe current research is based on computational study of the Death Domain (DD) superfamily of proteins involved in the cytoplasmic inflammasome complexes associated with apoptosis and inflammation in response to the nanoparticle ...
Identification of BACE1 inhibitors from Panax ginseng saponins-An Insilco approach
BACE1, a @b secretase candidate enzyme, initiates the Alzheimer's disease (AD) pathogenesis via amyloid @b (A@b) peptide production serving as a potential therapeutic target. Previous experimental evidence suggested that ginsenosides, a key component of ...
Pharmacoinformatics exploration of polyphenol oxidases leading to novel inhibitors by virtual screening and molecular dynamic simulation study
Display Omitted The 3D structures of PPO1 and PPO2 were built through homology modelling, while PPO3 and PPO4 were retrieved from PDB.Pharmacophore modelling was performed using LigandScout 3.1 software and top five models were selected to screen the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
September 2022
450 pages
ISBN:9781450395977
DOI:10.1145/3549737
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 September 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
SETN 2022
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 72Total Downloads
- Downloads (Last 12 months)34
- Downloads (Last 6 weeks)4
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format