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

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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
- Chinese Journal of Polymer Science Vol. 42, Issue 12, Pages: 2059-2068(2024)
- 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：
- DOI：10.1007/s10118-024-3237-y
  CLC： M
- Received：27 June 2024，
  
  Revised：2024-09-10，
  
  Accepted：11 September 2024，
  
  Online First：11 November 2024，
  
  Published：01 December 2024
- Accepted：
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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.

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references

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