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Exploring blood-brain barrier passage using atomic weighted vector and machine learning

dc.contributor.authorMartínez-López, Yoan
dc.contributor.authorPhoobane, Paulina
dc.contributor.authorJauriga, Yanaima
dc.contributor.authorCastillo-Garit, J A
dc.contributor.authorRodríguez-Gonzalez, Ansel Y.
dc.contributor.authorMartínez-Santiago, Oscar
dc.contributor.authorBarigye, Stephen J.
dc.contributor.authorMadera, Julio
dc.contributor.authorRodríguez-Maya, Noel Enrique
dc.contributor.authorDuchowicz, Pablo
dc.date.accessioned2024-12-06T14:14:46Z
dc.date.available2024-12-06T14:14:46Z
dc.date.issued2024
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/5802
dc.description.abstractContext This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood–brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classifcation and regression techniques, to predict BBB passage and molecular activity. Notably, classifcation models such as GBM-AWV (accuracy=0.801), GLM-CN (accuracy=0.808), SVMPoly-means (accuracy=0.980), SVMPoly-AC (accuracy=0.980), SVMPoly-MI_TI_SI (accuracy=0.900), SVMPoly-GI (accuracy=0.900), RF-means (accuracy=0.870), and GLM-means (accuracy=0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R2=0.902), IB-IBK (R2=0.82), IB-Kstar (R2=0.834), IB-MLP (R2=0.843), and DRF-AWV (R2=0.810) exhibit strong performance in predicting molecular activity. The results show that classifcation models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the efectiveness of machine learning in predicting BBB permeability. Methods The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and alidated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceJournal of Molecular Modeling, 30(11), 393es_CL
dc.subjectAtomic weighted vectorses_CL
dc.subjectMachine learninges_CL
dc.subjectBlood–brain barrieres_CL
dc.titleExploring blood-brain barrier passage using atomic weighted vector and machine learninges_CL
dc.typeArticlees_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionIsies_CL
dc.ucm.urispringerlink.ucm.elogim.com/article/10.1007/s00894-024-06188-5#Abs1es_CL
dc.ucm.doidoi.org/10.1007/s00894-024-06188-5es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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