Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties

Ivan Zlobin; Nikita Toroptsev; Gleb Averochkin; Alexander Pavlov

doi:10.1007/s10118-024-3237-y

Your Location：

Home >

Browse articles >

Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties

RESEARCH ARTICLE | Updated：2024-12-03

- Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties
- Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties
- 高分子科学（英文版） 2024年42卷第12期页码：2059-2068
- Affiliations：
  
  a.Center NTI "Digital Materials Science: New Materials and Substances", Bauman Moscow State Technical University, 2S1 Baumanskaya St., 5/1, 105005 Moscow, Russia
  b.A. N. Nesmeyanov Institute of Organoelement Compounds of Russian Academy of Sciences, Vavilova St., 28, Bld. 1, 119334 Moscow, Russia
- Author bio：
  
  zlobin.is@phystech.edu
- Funds：
  
  This work was carried out within the framework of the program of state support for the centers of the National Technology Initiative (NTI) on the basis of educational institutions of higher education and scientific organizations (Center NTI "Digital Materials Science: New Materials and Substances" on the basis of the Bauman Moscow State Technical University). The authors are grateful to the head of the Laboratory of Molecular modeling of Materials Eugeny Alexandrov for fruitful discussions of the results.;Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-024-3237-y.
- DOI：10.1007/s10118-024-3237-y
  中图分类号： M
- 收稿：2024-06-27，
  
  修回：2024-09-10，
  
  录用：2024-09-11，
  
  网络首发：2024-11-11，
  
  纸质出版：2024-12-01
- Accepted：
Scan QR Code
Zlobin, I.; Toroptsev, N.; Averochkin, G.; Pavlov, A. Pre-trained Mol2Vec embeddings as a tool for predicting polymer properties. Chinese J. Polym. Sci. 2024, 42, 2059–2068

Ivan Zlobin, Nikita Toroptsev, Gleb Averochkin, et al. Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties[J]. Chinese Journal of Polymer Science, 2024, 42(12): 2059-2068.
Zlobin, I.; Toroptsev, N.; Averochkin, G.; Pavlov, A. Pre-trained Mol2Vec embeddings as a tool for predicting polymer properties. Chinese J. Polym. Sci. 2024, 42, 2059–2068 DOI： 10.1007/s10118-024-3237-y.

Ivan Zlobin, Nikita Toroptsev, Gleb Averochkin, et al. Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties[J]. Chinese Journal of Polymer Science, 2024, 42(12): 2059-2068. DOI： 10.1007/s10118-024-3237-y.

摘要

The work is devoted to utilizing pre-trained Mol2Vec embeddings for the polymer properties prediction on a small dataset. The graphical abstract shows the comparison between performance metrics of model depending on the type of input.

Abstract

Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer materials. To date

several approaches have been implemented to represent the chemical structure in machine learning models

among which Mol2Vec embeddings have attracted considerable attention in the cheminformatics community since their introduction in 2018. However

for small datasets

the use of chemical structure representations typically increases the dimensionality of the input dataset

resulting in a decrease in model performance. Furthermore

the limited diversity of polymer chemical structures hinders the training of reliable embeddings

necessitating complex task-specific architecture implementations. To address these challenges

we examined the efficacy of Mol2Vec pre-trained embeddings in deriving vectorized representations of polymers. This study assesses the impact of incorporating Mol2Vec compound vectors into the input features on the efficacy of a model reliant on the physical properties of 214 polymers. The results will hopefully highlight the potential for improving prediction accuracy in polymer studies by incorporating pre-trained embeddings or promote their utilization when dealing with modestly sized polymer databases.

关键词

Keywords

references

Chen, L.; Pilania, G.; Batra, R.; Huan, T. D.; Kim, C.; Kuenneth, C.; Ramprasad, R. Polymer informatics: current status and critical next steps. Mater. Sci. Eng.: R: Reports 2021 , 144 , 100595..

Fang, W.; Mu, Z.; He, Y.; Kong, K.; Jiang, K.; Tang, R.; Liu, Z. Organic-inorganic covalent-ionic molecules for elastic ceramic plastic. Nature 2023 , 619 , 293−299..

Shi, Q.; Deng, Z.; Hou, M.; Hu, X.; Liu, S. Engineering precise sequence-defined polymers for advanced functions. Prog. Polym. Sci. 2023 , 141 , 101677..

Shao, Y.; Gao, Y.; Sun, R.; Zhang, M.; Min, J. A versatile and low-cost polymer donor based on 4-chlorothiazole for highly efficient polymer solar cells. Adv. Mater. 2023 , 35 , 2208750..

Aristova, V. A.; Bezlepkina, K. A.; Klokova, K. S.; Ardabevskaia, S. N.; Drozdov, F. V.;Cherkaev, G. V.; Milenin, S. A. Environmentally friendly synthesis and self-catalytic hydrolysis of triazole-modified organosilanes for polysiloxane production. Chemistry Select 2023 , 8 , e202303431..

Aysin, R. R.; Galkin, K. I. Impact of backbone substitution on organocatalytic activity of sterically encumbered NHC in benzoin condensation. Molecules 2024 , 29 , 1704..

Matyjaszewski, K. Architecturally complex polymers with controlled heterogeneity. Science 2011 , 333 , 1104−1105..

Vasilyeva, A. A.; Ryzhkov, A. I.; Cherkaev, G. V.; Drozdov, F. V.; Muzafarov, A. M. Silicone films with azo dyes moieties based on eugenol with response to Cu 2+ metal ions. Mater. Chem. Phys. 2024 , 318 , 129248..

Balenko, N.; Shibaev, V.; Bobrovsky, A. Polymer dispersed cholesteric liquid crystals with combined photo- and mechanochromic response. J. Molecular Liquids 2024 , 401 , 124637..

Jr, C. E. C.; Seymour, R. B. Structure—Property Relationships in Polymers ; Springer Science & amp; Business Media, 2012 ..

Hart, L. F.; Hertzog, J. E.; Rauscher, P. M.; Rawe, B. W.; Tranquilli, M. M.; Rowan, S. J. Material Properties and Applications of Mechanically Interlocked Polymers. Nat. Rev. Mater. 2021 , 6 , 508−530..

Zheng, B.; Huo, L.; Li, Y. Benzodithiophenedione-based polymers: recent advances in organic photovoltaics. NPG Asia Mater. 2020 , 12 , 1−22..

Zheng, Y.; Pan, P. Crystallization of Biodegradable and biobased polyesters: polymorphism, cocrystallization, and structure-proper ty relationship. Prog. Polym. Sci. 2020 , 109 , 101291..

Hu, R.; Qin, A.; Tang, B. Z. AIE polymers: synthesis and applications. Prog. Polym. Sci. 2020 , 100 , 101176..

Binder, K. Monte Carlo and Molecular Dynamics Simulations in Polymer Science ; Oxford University Press, 1995 ..

Bergstrom, J. S. Mechanics of Solid Polymers: Theory and Computational Modeling ; William Andrew, 2015 ..

Ruipérez, F. Application of quantum chemical methods in polymer chemistry. Int. Rev. Phys. Chem. 2019 , 38 , 343−403..

Schmid, F. Understanding and modeling polymers: the challenge of multiple scales. ACS Polym. Au 2023 , 3 , 28−58..

Giessen, E. van der; Schultz, P. A.; Bertin, N.; Bulatov, V. V.; Cai, W.; Csányi, G.; Foiles, S. M.; Geers, M. G. D.; González, C.; Hütter, M.; Kim, W. K.; Kochmann, D. M.; LLorca, J.; Mattsson, A. E.; Rottler, J.; Shluger, A.; Sills, R. B.; Steinbach, I.; Strachan, A.; Tadmor, E. B. Roadmap on multiscale materials modeling. Modelling Simul. Mater. Sci. Eng. 2020 , 28 , 043001..

Nguyen, D.; Tao, L.; Li, Y. Integration of machine learning and coarse-grained molecular simulations for polymer materials: physical understandings and molecular design. Front. Chem . 2022 , 9 . DOI:10.3389/fchem.2021.820417..

Xu, C.; Wang, Y.; Barati Farimani, A. TransPolymer: a transformer-based language model for polymer property predictions. npj Comput. Mater. 2023 , 9 , 64..

Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018 , 559 , 547−555..

S. Clegg, P. Characterising soft matter using machine learning. Soft Matter 2021 , 17 , 3991−4005..

Barnett, J. W.; Bilchak, C. R.; Wang, Y.; Benicewicz, B. C.; Murdock, L. A.; Bereau, T.; Kumar, S. K. Designing exceptional gas-separation polymer membranes using machine learning. Sci. Adv. 2020 , 6 , eaaz4301..

Kuenneth, C.; Lalonde, J.; Marrone, B. L.; Iverson, C. N.; Ramprasad, R.; Pilania, G. Bioplastic design using multitask deep neural networks. Commun. Mater. 2022 , 3 , 1−10..

Bone, J. M.; Childs, C. M.; Menon, A.; Póczos, B.; Feinberg, A. W.; LeDuc, P. R.; Washburn, N. R. Hierarchical machine learning for high-fidelity 3D printed biopolymers. ACS Biomater. Sci. Eng. 2020 , 6 , 7021−7031..

Martin, T. B.; Audus, D. J. Emerging trends in machine learning: a polymer perspective. ACS Polym. Au 2023 , 3 , 239−258..

Pilania, G.; Wang, C.; Jiang, X.; Rajasekaran, S.; Ramprasad, R. Accelerating materials property predictions using machine learning. Sci. Rep. 2013 , 3 , 2810..

Agrawal,A.; Choudhary, A. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in ma terials science. APL Materials 2016 , 4 , 053208..

Karuth, A.; Alesadi, A.; Xia, W.; Rasulev, B. Predicting glass transition of amorphous polymers by application of cheminformatics and molecular dynamics simulations. Polymer 2021 , 218 , 123495..

Li, D.; Dong, C.; Chen, Z.; Dong, Y.; Liu, J. A Combinatorial machine-learning-driven approach for predicting glass transition temperature based on numerous molecular descriptors. Molecular Simulation 2023 , 49 , 617−627..

Khan, P. M.; Rasulev, B.; Roy, K. QSPR modeling of the refractive index for diverse polymers using 2D descriptors. ACS Omega 2018 , 3 , 13374−13386..

Mannodi-Kanakkithodi, A.; Chandrasekaran, A.; Kim, C.; Huan, T. D.; Pilania, G.; Botu, V.; Ramprasad, R. Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond. Materials Today 2018 , 21 , 785−796..

Kopal, I.; Harničárová, M.; Valíček, J.; Krmela, J.; Lukáč, O. Radial basis function neural network-based modeling of the dynamic thermo-mec hanical response and damping behavior of thermoplastic elastomer systems. Polymers 2019 , 11 , 1074..

Xu, P.; Lu, T.; Ju, L.; Tian, L.; Li, M.; Lu, W. Machine learning aided design of polymer with targeted band gap based on DFT computation. J. Phys. Chem. B 2021 , 125 , 601−611..

Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 1988 , 28 , 31−36..

Warr, W. A. Representation of chemical structures. WIREs Comput. Mol. Sci. 2011 , 1 , 557−579..

Heller, S.; McNaught, A.; Stein, S.; Tchekhovskoi, D.; Pletnev, I. InChI - the worldwide chemical structure identifier standard. J Cheminform 2013 , 5 , 7..

Daylight Theory: SMARTS - A Language for Describing Molecular Patterns . https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html (accessed 2024-06-01).

Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 2010 , 50 , 742−754..

Landrum, G. RDKit Documentation.

Chen, G.; Shen, Z.; Iyer, A.; Ghumman, U. F.; Tang, S.; Bi, J.; Chen, W.; Li, Y. Machine-learning-assisted De Novo design of organic molecules and polymers: opportunities and challenges. Polymers 2020 , 12 , 163..

Sun, L. W.; Li, H.; Zhang, X. Q.; Gao, H. B.; Luo, M. B. Identifying conformation states of polymer through unsupervised machine learning. Chinese J. Polym. Sci. 2020 , 38 , 1403−1408..

Tao, L.; Chen, G.; Li, Y. Machine learning discovery of high-temperature polymers. Patterns 2021 , 2 , 100225..

Tao, L.; Varshney, V.; Li, Y. Benchmarking machine learning models for polymer informatics: an example of glass transition temperature. J. Chem. Inf. Model. 2021 , 61 , 5395−5413..

Aldeghi, M.; W. Coley, C. A Graph representation of molecular ensembles for polymer property prediction. Chem. Sci. 2022 , 13 , 10486−10498..

Liu, T. L.; Liu, L. Y.; Ding, F.; Li, Y. Q. A machine learning study of polymer-solvent interactions. Chinese J. Polym. Sci. 2022 , 40 , 834−842..

Zhao, Y.; J. Mulder, R.; Houshyar, S.; C. Le, T. A review on the application of molecular descriptors and machine learning in polymer design. Polym. Chem. 2023 , 14 , 3325−3346..

Ding, F.; Liu, L. Y.; Liu, T. L.; Li, Y. Q.; Li, J. P.; Sun, Z. Y. Predicting the mechanical properties of polyurethane elastomers using machine learning. Chinese J. Polym. Sci. 2023 , 41 , 422−431..

Jaeger, S.; Fulle, S.; Turk, S. Mol2vec: unsupervised machine learning approach with chemical intuition. J. Chem. Inf. Model. 2018 , 58 , 27−35..

Irwin, J. J .; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 2012 , 52 , 1757−1768..

Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research 2012 , 40 , D1100−D1107..

Shibayama, S.; Funatsu, K. Industrial case study: identification of important substructures and exploration of monomers for the rapid design of novel network polymers with distributed representation. BCSJ 2021 , 94 , 112−121..

Huang, X.; Ma, S.; Zhao, C. Y.; Wang, H.; Ju, S. Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors. npj Comput. Mater. 2023 , 9 , 191..

Chemical Retrieval on the Web (CROW). Available online: http://www.polymerdatabase.com/ (accessed on 12 September 2023).

Chi, M.; Gargou ri, R.; Schrader, T.; Damak, K.; Maâlej, R.; Sierka, M. Atomistic descriptors for machine learning models of solubility parameters for small molecules and polymers. Polymers 2022 , 14 , 26..

Hatakeyama-Sato, K.; Watanabe, S.; Yamane, N.; Igarashi, Y.; Oyaizu, K. Using GPT-4 in parameter selection of polymer informatics: improving predictive accuracy amidst data scarcity and ‘ugly duckling’ dilemma. Digital Discovery 2023 , 2 , 1548−1557..

Otsuka, S.; Kuwajima, I.; Hosoya, J.; Xu, Y.; Yamazaki, M. PoLyInfo: polymer database for polymeric materials design. In 2011 International Conference on Emerging Intelligent Data and Web Technologies ; 2011 ; pp 22–29. DOI:10.1109/EIDWT.2011.13..

Jarrett, D.; Cebere, B.; Liu, T.; Curth, A.; van der Schaar, M. HyperImpute: generalized iterative imputation with automatic model selection. arXiv June 15, 2022. DOI:10.48550/arXiv.2206.07769.

Liu, F. T.; Ting, K. M.; Zhou, Z. H. Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining ; 2008 ; pp 413–422. DOI:10.1109/ICDM.2008.17..

Borrohou, S.; Fissoune, R.; Badir, H. Data cleaning survey and challenges – improving outlier detection algorithm in machine learning. J. Smart Cities and Society 2023 , 2 , 125−140..

Molecular Machine Learning with DeepChem - ProQuest . https://www.proquest.com/openview/9c0e06a343233b48d962991d19873ed8/1?pq-origsite=gscholar & amp;cbl=18750 & amp;diss=y (accessed 2024-08-25).

Cortes, C.; Vapnik, V. Support-vector networks. Mach Learn 1995 , 20 , 273−297..

Breiman, L. Random forests. Machine Learning 2001 , 45 , 5−32..

Geladi, P.; Kowalski, B. R. Partial least-squares regression: a tutorial. Analytica Chimica Acta 1986 , 185 , 1−17..

Zou, H.; Hastie, T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology 2005 , 67 , 301−320..

Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; KDD ’16; Association for Computing Machinery: New York, NY, USA, 2016 ; pp. 785–794..

Jamieson, K.; Talwalkar, A. Non-stochastic best arm identification and hyperparameter optimization. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics ; PMLR, 2016 ; pp 240–248..

Saeki, S.; Yamaguchi, T. Semiempirical examination of the van Der Waals and Tonks models for polymers and simple liquids. Polymer 1987 , 28 , 484−488..

Barton, A. F. M. Solubility parameters . ACS Publications. DOI:10.1021/cr60298a003.

Venkatram, S.; Kim, C.; Chandrasekaran, A.; Ramprasad, R. Critical assessment of the hildebrand and hansen solubility parameters for polymers. J. Chem. Inf. Model. 2019 , 59 , 4188−4194..

Shi, X.; Wong, Y. D.; Li, M. Z. F.; Palanisamy, C.; Chai, C. A feature learning approach based on xgboost for driving assessment and risk prediction. Accident Anal. Prevention 2019 , 129 , 170−179..

Liu, T.; Liu, L.; Cui, F.; Ding, F.; Zhang, Q.; Li, Y. Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning. J. Mater. Chem. A 2020 , 8 , 21862−21871..

Polymer Handbook , 4. ed.; Brandrup, J., Ed.; A Wiley-Interscience publication; Wiley: New York Weinheim, 1999 ..

Bicerano, J. Prediction of Polymer Properties , 3 rd Ed.; CRC Press: Boca Raton, 2002 ..

Jha, A.; Chandrasekaran, A.; Kim, C.; Ramprasad, R. Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures. Modelling Simul. Mater. Sci. Eng. 2019 , 27 , 024002..

Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014 , 1 , 140022..

Wu, S.; Kondo, Y.; Kakimoto, M.; Yang, B.; Yamada, H.; Kuwajima, I.; Lambard, G.; Hongo, K.; Xu, Y.; Shiomi, J.; Schick, C.; Morikawa, J.; Yoshida, R. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Comput. Mater. 2019 , 5 , 1−11..

Volgin, I. V.; Batyr, P. A.; Matseevich, A. V.; Dobrovskiy, A. Yu.; Andreeva, M. V.; Nazarychev, V. M.; Larin, S. V.; Goikhman, M. Ya.; Vizilter, Y. V.; Askadskii, A. A.; Lyulin, S. V. Machine learning with enormous “synthetic” data sets: predicting glass transition temperature of polyimides using graph convolutional neural networks. ACS Omega 2022 , 7 , 43678−43691..

Lightstone, J. P.; Chen, L.; Kim, C.; Batra, R.; Ramprasad, R. Refractive index prediction models for polymers using machine learning. J. Appl. Phys. 2020 , 127 , 215105..

Najeeb, J.; Shah, S. S. A.; Tahir, M. H.; I. Ha nafy, A.; M. El-Bahy, S.; M. El-Bahy, Z. Machine learning assisted designing of polymers and refractive index prediction: easy and fast screening of polymers from chemical space. Mater. Chem. Phys. 2024 , 324 , 129685..

Lee, F. L.; Park, J.; Goyal, S.; Qaroush, Y.; Wang, S.; Yoon, H.; Rammohan, A.; Shim, Y. Comparison of machine learning methods towards developing interpretable polyamide property prediction. Polymers 2021 , 13 , 3653..

Mark, J. Physical Properties of Polymers ; Cambridge University Press, 2004 ..

Bhowmik, R.; Sihn, S.; Pachter, R.; Vernon, J. P. Prediction of the specific heat of polymers from experimental data and machine learning methods. Polymer 2021 , 220 , 123558..

Kazemi-Khasragh, E.; González, C.; Haranczyk, M. Toward diverse polymer property prediction using transfer learning. Comput. Mater. Sci. 2024 , 244 , 113206..

浏览量

369

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Self-powered Posture Recognition Based on Thermoelectric Hydrogel

Prediction of Stress-strain Behavior for Polyurethane Elastomers Based on Machine Learning

Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine

Advances in Polymer-based Circularly Polarized Luminescent Materials

Preparation and Chiral Recognition Ability of Crystalline Cellulose Phenylcarbamate