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dc.contributor.author
Ponzoni, Ignacio
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dc.contributor.author
Páez Prosper, Juan Antonio
dc.contributor.author
Campillo, Nuria E.
dc.date.available
2023-10-03T12:52:00Z
dc.date.issued
2023-07
dc.identifier.citation
Ponzoni, Ignacio; Páez Prosper, Juan Antonio; Campillo, Nuria E.; Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery; John Wiley & Sons; WIREs Computational Molecular Science; 7-2023; 1-27
dc.identifier.issn
1759-0876
dc.identifier.uri
http://hdl.handle.net/11336/213904
dc.description.abstract
Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery. However, it is still critical for their adoption by the medicinal chemistry community to achieve models that, in addition to achieving high performance in their predictions, can be trusty explained to the end users in terms of their knowledge and background. Therefore, the investigation and development of explainable artificial intelligence (XAI) methods have become a key topic to address this challenge. For this reason, a comprehensive literature review about explanation methodologies for AI based models, focused in the field of drug discovery, is provided. In particular, an intuitive overview about each family of XAI approaches, such as those based on feature attribution, graph topologies, or counterfactual reasoning, oriented to a wide audience without a strong background in the AI discipline is introduced. As the main contribution, we propose a new taxonomy of the current XAI methods, which take into account specific issues related with the typical representations and computational problems study in the design of molecules. Additionally, we also present the main visualization strategies designed for supporting XAI approaches in the chemical domain. We conclude with key ideas about each method category, thoroughly providing insightful analysis about the guidelines and potential benefits of their adoption in medical chemistry.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DEEP LEARNING
dc.subject
DRUG DISCOVERY
dc.subject
EXPLAINABLE ARTIFICIAL INTELLIGENCE
dc.subject
VISUALIZATION
dc.subject.classification
Ciencias de la Información y Bioinformática
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-10-03T10:21:06Z
dc.identifier.eissn
1759-0884
dc.journal.pagination
1-27
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Hoboken, Nueva Jersey
dc.description.fil
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
dc.description.fil
Fil: Páez Prosper, Juan Antonio. Consejo Superior de Investigaciones Científicas; España
dc.description.fil
Fil: Campillo, Nuria E.. Instituto de Ciencias Matemáticas; España. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
dc.journal.title
WIREs Computational Molecular Science
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1681
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/wcms.1681
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