Logo do repositório
 
Publicação

A Chemoinformatics Investigation of Spectral and Quantum Chemistry Patterns for Discovering New Drug Leads from Natural Products Targeting the PD-1/PD-L1 Immune Checkpoint, with a Particular Focus on Naturally Occurring Marine Products

2025-06-10Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorRabelo, Henrique
dc.contributor.authorTsimiante, Ayana
dc.contributor.authorBinev, Yuri
dc.contributor.authorPereira, Florbela
dc.contributor.institutionLAQV@REQUIMTE
dc.contributor.institutionDQ - Departamento de Química
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2025-08-01T21:20:33Z
dc.date.available2025-08-01T21:20:33Z
dc.date.issued2025-06-10
dc.descriptionFunding Information: This research was funded by Fundação para a Ciência e Tecnologia (FCT) Portugal, grant number UIDB/50006/2020 (LAQV-REQUIMTE). F.P. gratefully acknowledges FCT for an assistant research position (CEECIND/01649/2021). Publisher Copyright: © 2025 by the authors.
dc.description.abstract(1) Background: Although the field of natural product (NP) drug discovery has been extensively developed, there are still several bottlenecks hindering the development of drugs from NPs. The PD-1/PD-L1 immune checkpoint axis plays a crucial role in immune response regulation. Therefore, drugs targeting this axis can disrupt the interaction and enable immune cells to continue setting up a response against the cancer cells. (2) Methods: We have explored the immuno-oncological activity of NPs targeting the PD-1/PD-L1 immune checkpoint by estimating the half maximal inhibitory concentration (IC50) through molecular docking scores and predicting it using machine learning (ML) models. The LightGBM (Light Gradient-Boosted Machine), a tree-based ML technique, emerged as the most effective approach and was used for building the quantitative structure–activity relationship (QSAR) classification model. (3) Conclusions: The model incorporating 570 spectral descriptors from NMR SPINUS was selected for the optimization process, and this approach yielded results for the external test set with a sensitivity of 0.74, specificity of 0.81, overall predictive accuracy of 0.78, and Matthews correlation coefficient (MCC) of 0.55. The strategy used here for estimating the IC50 from docking scores and predicting it through ML models appears to be a promising approach for pure compounds. Nevertheless, further optimization is indicated, particularly through the simulation of the spectra of mixtures by combining the spectra of individual compounds.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent19
dc.format.extent2999382
dc.identifier.doi10.3390/md23060247
dc.identifier.issn1660-3397
dc.identifier.otherPURE: 121728168
dc.identifier.otherPURE UUID: 78ce0f96-aebc-4cc0-a78b-069a0ae6e1de
dc.identifier.otherScopus: 105009267688
dc.identifier.otherPubMed: 40559656
dc.identifier.otherWOS: 001515968900001
dc.identifier.otherPubMed: 40559656
dc.identifier.otherPubMedCentral: PMC12194479
dc.identifier.otherORCID: /0000-0003-4392-4644/work/189075601
dc.identifier.urihttp://hdl.handle.net/10362/185920
dc.identifier.urlhttps://www.scopus.com/pages/publications/105009267688
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001515968900001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50006%2F2020/PT
dc.subjectImmuno-oncology
dc.subjectMachine learning (ML) techniques
dc.subjectMolecular docking
dc.subjectNatural products (NPs)
dc.subjectNuclear magnetic resonance (NMR)
dc.subjectPD-1/PD-L1 immune checkpoint
dc.subjectQuantitative structure–activity relationship (QSAR) models
dc.subjectVirtual screening
dc.subjectPharmaceutical Science
dc.subjectDrug Discovery
dc.subjectPharmacology, Toxicology and Pharmaceutics (miscellaneous)
dc.subjectSDG 3 - Good Health and Well-being
dc.titleA Chemoinformatics Investigation of Spectral and Quantum Chemistry Patterns for Discovering New Drug Leads from Natural Products Targeting the PD-1/PD-L1 Immune Checkpoint, with a Particular Focus on Naturally Occurring Marine Productsen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue6
degois.publication.lastPage19
degois.publication.titleMarine Drugs
degois.publication.volume23
dspace.entity.typePublication
oaire.awardNumberUIDB/50006/2020
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50006%2F2020/PT
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication186df1fb-72e1-4b5e-bb56-1760cf233cbe
relation.isProjectOfPublication.latestForDiscovery186df1fb-72e1-4b5e-bb56-1760cf233cbe

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Rabelo_H._Tsimiante_A._Binev_Y._Pereira_F._2025_..pdf
Tamanho:
2.86 MB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

Home collection (FCT)