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Cited by 33Organometallic Complex Synthesis and CatalysisComputational Drug Discovery MethodsMachine Learning in Materials Science

Polymerization Activity Prediction of Zirconocene Single-Site Catalysts Using 3D Quantitative Structure–Activity Relationship Modeling

Martínez, Sonia, Cruz, Víctor L., Ramos, Javier, Martínez-Salazar, Javier·2012·Organometallics
DOI 10.1021/om2007776
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Abstract

We have applied 3D-QSAR chemometric tools to analyze the polymerization activity of a series of analogous zirconocene catalysts with different substitution patterns in the cyclopentadienyl rings. The selection of the most stable conformers was performed by means of a conformational analysis of the different catalysts in a step previous to the QSAR study. The calculated QSAR model has been assessed in robustness following a progressive scrambling protocol that helps to check redundancy in the descriptor space. This model presents a good predictive ability as tested with a set of four catalysts. The final model is controlled mostly by steric contributions, which can be related to the weakness of the catalyst–cocatalyst interaction.

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  • Early efforts in this area can be traced back to the use of linear models and descriptors.

    “Early efforts in this area can be traced back to the use of linear models and descriptors (71).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [71]

  • Other 3D-focused approaches include the development of maps of electrostatics with probe molecules, an approach that has recently gained favor in deep learning models for predicting gas storage characteristics of MOFs, or experimental homogeneous catalyst activities, where it is sometimes called stereocartography.

    “Other 3D-focused approaches include the development of maps of electrostatics with probe molecules (71), an approach that has recently gained favor in deep learning models for predicting gas storage characteristics of MOFs (80), or experimental homogeneous catalyst activities (81), where it is sometimes called stereocartography (82).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [71]

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Authors
Martínez, S., Cruz, V. L., Ramos, J., & Martínez-Salazar, J.
Affiliations
  • Consejo Superior de Investigaciones Científicas (ES)
  • BIOPHYM, Departamento de Física Macromolecular, Instituto de Estructura de la Materia, CSIC, Serrano 113bis, 28006-Madrid, Spain
Corresponding author
Cruz, Víctor L.
ORCID
Venue
Organometallics
Publisher
American Chemical Society (ACS)
Published
2012-02-03
Volume
31
Issue
5
Pages
1673-1679
ISSN
  • 0276-7333 (ISSN-L)
  • 1520-6041
DOI
10.1021/om2007776
OpenAlex
W2327704256
Cited by
33
References
39
Citations by year
2026: 4 · 2025: 2 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 6 · 2018: 3 · 2017: 2 · 2016: 2 · 2015: 3 · 2014: 2
FWCI
0.74
Type
journal article
Language
en
Open access
closed
Locations
Organometallics (publishedVersion)
Funders
Universidad Rey Juan Carlos
Topics
  • Physical Sciences › Chemistry › Organic Chemistry › Organometallic Complex Synthesis and Catalysis
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
zirconocene catalysts, single-site catalysts, polymerization activity, 3D-QSAR, cyclopentadienyl ligands, conformational analysis, steric effects, descriptor redundancy, olefin polymerization

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