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Enhancing bioactive compound classification through the synergy of fourier-transform infrared spectroscopy and advanced machine learning methods

2024-05-09Journal article Open access
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dc.contributor.authorSampaio, Pedro
dc.contributor.authorCalado, Cecília
dc.date.accessioned2024-11-04T12:09:19Z
dc.date.available2024-11-04T12:09:19Z
dc.date.issued2024-05-09
dc.description.abstractBacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds’ impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSampaio Pedro N, Calado Cecília RC – Enhancing bioactive compound classification through the synergy of fourier-transform infrared spectroscopy and advanced machine learning methods. Antibiotics. 2024, 13(5), 428; https://doi.org/10.3390/antibiotics13050428pt_PT
dc.identifier.doi10.3390/antibiotics13050428pt_PT
dc.identifier.issn2079-6382
dc.identifier.urihttp://hdl.handle.net/10400.21/17828
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2079-6382/13/5/428pt_PT
dc.subjectantimicrobialpt_PT
dc.subjectCynara cardunculuspt_PT
dc.subjectmachine learningpt_PT
dc.subjectMIR-Spectroscopypt_PT
dc.subjectPCApt_PT
dc.subjectPLS-DApt_PT
dc.subjectSVMpt_PT
dc.subjectKNNpt_PT
dc.subjectBPNpt_PT
dc.titleEnhancing bioactive compound classification through the synergy of fourier-transform infrared spectroscopy and advanced machine learning methodspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage17pt_PT
oaire.citation.issue5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleAntibioticspt_PT
oaire.citation.volume13pt_PT
person.familyNameSampaio
person.familyNameCalado
person.givenNamePedro
person.givenNameCecília
person.identifier1251979
person.identifier130332
person.identifier.ciencia-idAF12-6ABA-43D8
person.identifier.ciencia-id9418-E320-3177
person.identifier.orcid0000-0003-2917-4904
person.identifier.orcid0000-0002-5264-9755
person.identifier.ridE-2102-2014
person.identifier.scopus-author-id24178064100
person.identifier.scopus-author-id6603163260
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication303f3e22-ec1a-4243-9ded-98c647776e6a
relation.isAuthorOfPublicatione8577257-c64c-4481-9b2b-940fedb360cc
relation.isAuthorOfPublication.latestForDiscoverye8577257-c64c-4481-9b2b-940fedb360cc

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