Usage of model combination in computational toxicology

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  • dc.contributor.author Rodríguez-Belenguer, Pablo
  • dc.contributor.author March-Vila, Eric
  • dc.contributor.author Pastor Maeso, Manuel
  • dc.contributor.author Mangas Sanjuan, Victor
  • dc.contributor.author Soria Olivas, Emilio
  • dc.date.accessioned 2023-12-15T10:28:54Z
  • dc.date.available 2023-12-15T10:28:54Z
  • dc.date.issued 2023
  • dc.description.abstract New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.
  • dc.description.sponsorship The authors received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 964537 (RISK-HUNT3R), which is part of the ASPIS cluster.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Rodríguez-Belenguer P, March-Vila E, Pastor M, Mangas-Sanjuan V, Soria-Olivas E. Usage of model combination in computational toxicology. Toxicol Lett. 2023 Nov 1;389:34-44. DOI: 10.1016/j.toxlet.2023.10.013
  • dc.identifier.doi http://dx.doi.org/10.1016/j.toxlet.2023.10.013
  • dc.identifier.issn 0378-4274
  • dc.identifier.uri http://hdl.handle.net/10230/58535
  • dc.language.iso eng
  • dc.publisher Elsevier
  • dc.relation.ispartof Toxicol Lett. 2023 Nov 1;389:34-44
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/964537
  • dc.rights © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword Complexities
  • dc.subject.keyword Machine learning
  • dc.subject.keyword Metamodel
  • dc.subject.keyword NAMs
  • dc.subject.keyword QSAR
  • dc.title Usage of model combination in computational toxicology
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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