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Analysis of institutional authors

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Article

Organic reaction mechanism classification using machine learning.

Publicated to:Nature. 613 (7945): 689-695 - 2023-01-01 613(7945), DOI: 10.1038/s41586-022-05639-4

Authors: Burés J; Larrosa I

Affiliations

Department of Chemistry, The University of Manchester, Manchester, UK. igor.larrosa@manchester.ac.uk. - Author
Department of Chemistry, The University of Manchester, Manchester, UK. jordi.bures@manchester.ac.uk. - Author

Abstract

A mechanistic understanding of catalytic organic reactions is crucial for the design of new catalysts, modes of reactivity and the development of greener and more sustainable chemical processes1-13. Kinetic analysis lies at the core of mechanistic elucidation by facilitating direct testing of mechanistic hypotheses from experimental data. Traditionally, kinetic analysis has relied on the use of initial rates14, logarithmic plots and, more recently, visual kinetic methods15-18, in combination with mathematical rate law derivations. However, the derivation of rate laws and their interpretation require numerous mathematical approximations and, as a result, they are prone to human error and are limited to reaction networks with only a few steps operating under steady state. Here we show that a deep neural network model can be trained to analyse ordinary kinetic data and automatically elucidate the corresponding mechanism class, without any additional user input. The model identifies a wide variety of classes of mechanism with outstanding accuracy, including mechanisms out of steady state such as those involving catalyst activation and deactivation steps, and performs excellently even when the kinetic data contain substantial error or only a few time points. Our results demonstrate that artificial-intelligence-guided mechanism classification is a powerful new tool that can streamline and automate mechanistic elucidation. We are making this model freely available to the community and we anticipate that this work will lead to further advances in the development of fully automated organic reaction discovery and development.

Keywords
AnthropologyArchaeologyArquitetura e urbanismoAstronomia / físicaBiodiversidadeBiotecnologíaCiência política e relações internacionaisCiências agrárias iCiências ambientaisCiências biológicas iCiências biológicas iiCiências biológicas iiiCiencias humanasCiencias socialesDemographyEconomiaEconomicsEngenharias iEngenharias iiEngenharias iiiEnvironmental studiesFarmaciaGeneral medicineGeneral o multidisciplinarGeociênciasGeografíaHuman geography and urban studiesInterdisciplinarInterdisciplinary research in the social sciencesMatemática / probabilidade e estatísticaMedia studies and communicationMedicina iMedicina iiMedicina iiiMedicina veterinariaMultidisciplinaryMultidisciplinary sciencesPsicologíaQuímicaSaúde coletivaScience and technology studiesSocial statistics and informaticsSociologíaSociologyZootecnia / recursos pesqueiros

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Nature due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 1/134, thus managing to position itself as a Q1 (Primer Cuartil), in the category Multidisciplinary Sciences. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 50.5, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Apr 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-04-30, the following number of citations:

  • Scopus: 63
  • Europe PMC: 9
  • OpenCitations: 51
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-30:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 236.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 234 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 155.48.
  • The number of mentions on the social network Facebook: 1 (Altmetric).
  • The number of mentions on the social network X (formerly Twitter): 173 (Altmetric).
Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Burés Amat, Jordi) .