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dc.contributor.authorCabrera-Andrade, Alejandro
dc.contributor.authorLópez-Cortés, Andrés
dc.contributor.authorMunteanu, Cristian-Robert
dc.contributor.authorPazos, A.
dc.contributor.authorPérez-Castillo, Yunierkis
dc.contributor.authorTejera, Eduardo
dc.contributor.authorArrasate, Sonia
dc.contributor.authorGonzález-Díaz, Humberto
dc.date.accessioned2020-11-24T10:12:54Z
dc.date.available2020-11-24T10:12:54Z
dc.date.issued2020-10-14
dc.identifier.citationCabrera-Andrade A, López-Cortés A, Munteanu CR, et al. Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds. ACS Omega. 2020; 5(42):27211-27220es_ES
dc.identifier.issn2470-1343
dc.identifier.urihttp://hdl.handle.net/2183/26747
dc.description.abstract[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; CTQ2016-74881-Pes_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; UNLC08-1E-002es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; UNLC13-13-3503es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2018/49es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 2017/16es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 2017/23es_ES
dc.description.sponsorshipGobierno Vasco; IT1045-16es_ES
dc.description.sponsorshipInstituto de Salud Carlos III; PI17/01826es_ES
dc.language.isoenges_ES
dc.publisherAmerican Chemical Societyes_ES
dc.relation.urihttps://doi.org/10.1021/acsomega.0c03356es_ES
dc.rightsThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.es_ES
dc.subjectAssayses_ES
dc.subjectChemical specificityes_ES
dc.subjectMolecular modelinges_ES
dc.subjectMathematical methodses_ES
dc.subjectBioactivityes_ES
dc.titlePerturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compoundses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleACS Omegaes_ES
UDC.volume5es_ES
UDC.issue42es_ES
UDC.startPage27211es_ES
UDC.endPage27220es_ES


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