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dc.contributor.authorRocha, Vinícius Nunes da-
dc.date.accessioned2026-10-26T12:48:16Z-
dc.date.available2026-10-26T12:48:16Z-
dc.date.issued2026-07-07-
dc.identifier.citationROCHA, Vinícius Nunes da. Arquitetura multifacetada em CADD integrando modelagem estatístico-estrutural e aprendizagem de máquina para predição da atividade modulatória de receptores NMDA. 2026. 623 f. Tese (Doutorado em Química) – Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica, 2026.pt_BR
dc.identifier.urihttp://rima.ufrrj.br/jspui/handle/20.500.14407/26776-
dc.description.abstractEsta tese propõe uma arquitetura multifacetada de planejamento de fármacos auxiliado por computador (CADD), integrando modelagem estatístico-estrutural e aprendizagem de máquina para classificar ligantes segundo um critério operacional de afinidade de ligação no receptor NMDA/GluN1–GluN2B. Considerando a relevância glutamatérgica do receptor NMDA em transtornos neurológicos, incluindo o transtorno de humor bipolar, o estudo concentra-se no sítio alostérico GluN1–GluN2B descrito para antagonistas e moduladores alostéricos negativos. A afinidade de ligação (Ki) foi utilizada como principal base experimental para a rotulagem, interpretada em conjunto com o contexto estrutural e farmacológico dos ligantes de referên- cia, sem ser tomada isoladamente como medida de função agonista, antagonista, de modulação alostérica negativa (NAM) ou de modulação alostérica positiva (PAM). O fluxo metodológico combinou curadoria química, padronização estrutural, docagem/redocagem molecular, extração de descritores físico-químicos, componentes da função de pontuação e fingerprints de intera- ção ligante–proteína, análise de coocorrências, perfis probabilísticos e modelagem de relações quantitativas estrutura–atividade (QSAR) por aprendizagem de máquina (ML), compondo uma matriz com 320 ligantes experimentais e 57 covariáveis. Modelos perceptron multicamadas (MLP), máquina de vetores de suporte (SVM) e eXtreme Gradient Boosting (XGBoost) foram treinados e comparados; o MLP foi descartado por sobreajuste, enquanto SVM e XGBoost apresentaram desempenho retrospectivo mais estável na validação cruzada interna e na avali- ação do conjunto retido. A triagem virtual é discutida em caráter prospectivo, de modo que os modelos selecionados possam ser futuramente aplicados a quimiotecas curadas e descritas pelo mesmo protocolo químico-computacional. Os resultados documentam a rastreabilidade do fluxo e indicam que a integração entre análise estatística, representação estrutural, mineração de interações e modelagem preditiva pode apoiar a priorização racional de ligantes candidatos antes de filtros de absorção, distribuição, metabolismo, excreção e toxicidade (ADME–Tox) e validação experimental prospectiva.pt_BR
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESpt_BR
dc.languageporpt_BR
dc.publisherUniversidade Federal Rural do Rio de Janeiropt_BR
dc.subjectreceptor NMDApt_BR
dc.subjecttranstorno de humor bipolarpt_BR
dc.subjectCADDpt_BR
dc.subjectaprendizagem de máquinapt_BR
dc.subjectQSARpt_BR
dc.subjectNMDA receptorpt_BR
dc.subjectbipolar disorderpt_BR
dc.subjectmachine learningpt_BR
dc.titleArquitetura multifacetada em CADD integrando modelagem estatístico-estrutural e aprendizagem de máquina para predição da atividade modulatória de receptores NMDApt_BR
dc.title.alternativeA multifaceted CADD architecture integrating statistical- structural modeling and machine learning to predict modulatory activity of NMDA re- ceptorsen
dc.typeTesept_BR
dc.description.abstractOtherThis thesis proposes a multifaceted computer-aided drug design (CADD) architecture that in- tegrates statistical-structural modeling and machine learning to classify ligands according to an operational binding-affinity criterion at the NMDA/GluN1–GluN2B receptor. Given the gluta- matergic relevance of the NMDA receptor in neurological disorders, including bipolar disorder, the study focuses on the GluN1–GluN2B allosteric site described for antagonists and negative allosteric modulators. Binding affinity (Ki) was used as the main experimental basis for labeling and was interpreted together with the structural and pharmacological context of the reference ligands, rather than as an isolated measure of agonism, antagonism, negative allosteric mod- ulation (NAM), or positive allosteric modulation (PAM). The methodological workflow com- bined chemical curation, structural standardization, molecular docking/redocking, extraction of physicochemical descriptors, scoring-function components, and protein–ligand interaction fin- gerprints, co-occurrence analysis, probabilistic profiles, and quantitative structure–activity re- lationship (QSAR) modeling by machine learning (ML), yielding a matrix of 320 experimental ligands and 57 covariates. Multilayer perceptron (MLP), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models were trained and compared; MLP was discarded due to overfitting, whereas SVM and XGBoost showed more stable retrospective performance in internal cross-validation and held-out-set evaluation. Virtual screening is discussed prospec- tively, so that the selected models may later be applied to curated chemical libraries described by the same chemical-computational protocol. The results document workflow traceability and indicate that integrating statistical analysis, structural representation, interaction mining, and predictive modeling can assist the rational prioritization of candidate ligands before absorp- tion, distribution, metabolism, excretion, and toxicity (ADME–Tox) filtering and prospective experimental validation.en
dc.contributor.advisor1Sant'Anna, Carlos Mauricio Rabello de-
dc.contributor.advisor1IDhttps://orcid.org/0000-0003-1989-5038pt_BR
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2087099684752643pt_BR
dc.contributor.referee1Sant'Anna, Carlos Mauricio Rabello de-
dc.contributor.referee1IDhttps://orcid.org/0000-0003-1989-5038pt_BR
dc.contributor.referee1Latteshttp://lattes.cnpq.br/2087099684752643pt_BR
dc.contributor.referee2Kümmerle, Arthur Eugen-
dc.contributor.referee2Latteshttp://lattes.cnpq.br/5598000938584486pt_BR
dc.contributor.referee3Albuquerque, Magaly Girão-
dc.contributor.referee3Latteshttp://lattes.cnpq.br/0780841238637304pt_BR
dc.contributor.referee4Guedes, Isabella Alvim-
dc.contributor.referee4IDhttps://orcid.org/0000-0002-9761-4804pt_BR
dc.contributor.referee4Latteshttp://lattes.cnpq.br/2265756121721735pt_BR
dc.contributor.referee5Tassinari, Wagner de Souza-
dc.contributor.referee5IDhttps://orcid.org/0000-0002-3799-1261pt_BR
dc.contributor.referee5Latteshttp://lattes.cnpq.br/3648148709641027pt_BR
dc.creator.IDhttps://orcid.org/0000-0002-2557-3871pt_BR
dc.creator.Latteshttp://lattes.cnpq.br/5217191640471435pt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentInstituto de Químicapt_BR
dc.publisher.initialsUFRRJpt_BR
dc.publisher.programPrograma de Pós-Graduação em Químicapt_BR
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