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Analysis of institutional authors

Arora, TAuthorDomingo-Almenara, XCorresponding Author

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January 5, 2026
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Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules

Publicated to: Analytical Chemistry. 96 (22): 9088-9096 - 2024-05-24 96(22), DOI: 10.1021/acs.analchem.4c00630

Authors:

de Cripan, SM; Arora, T; Olomi, A; Canela, N; Siuzdak, G; Domingo-Almenara, X
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Affiliations

Eurecat Technol Ctr Catalonia, Ctr Om Sci COS, Reus 43204 - Author
Scripps Res Inst, Scripps Ctr Metabol & Mass Spectrometry, Dept Chem Mol & Computat Biol - Author
Univ Rovira & Virgili, Dept Elect Elect & Control Engn DEEEA, Tarragona 43007 - Author
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Abstract

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.
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Keywords

Similarity

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Analytical Chemistry 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, 2024 there are still no calculated indicators, but in 2023, it was in position 10/111, thus managing to position itself as a Q1 (Primer Cuartil), in the category Chemistry, Analytical. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-01-20:

  • WoS: 7
  • Scopus: 6
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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 2026-01-20:

  • 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: 11.
  • 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: 11 (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: 4.
  • The number of mentions on the social network X (formerly Twitter): 8 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/20.500.11797/imarina9368765
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United States of America.

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 (de Cripan, SM) and Last Author (Domingo Almenara, Xavier).

the author responsible for correspondence tasks has been Domingo Almenara, Xavier.

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