Matches in SemOpenAlex for { <https://semopenalex.org/work/W3088615466> ?p ?o ?g. }
- W3088615466 endingPage "107377" @default.
- W3088615466 startingPage "107377" @default.
- W3088615466 abstract "The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as “chemoinformatics,” which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood–brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS). In this research, we attempt to solve the problem of BBB permeability by predicting compounds penetration to the CNS. To accomplish this goal: (i) First, an overview is provided to the field of chemoinformatics, its definition, applications, and challenges, (ii) Second, a broad view is taken to investigate previous machine-learning and deep-learning computational models to solve BBB permeability. Based on the analysis of previous models, three main challenges that collectively affect the classifier performance are identified, which we define as “the triple constraints”; subsequently, we map each constraint to a proposed solution, (iii) Finally, we conclude this endeavor by proposing a deep learning based Recurrent Neural Network model, to predict BBB permeability (RNN-BBB model). Our model outperformed other studies from the literature by scoring an overall accuracy of 96.53%, and a specificity score of 98.08%. The obtained results confirm that addressing the triple constraints substantially improves the classification model capability specifically when predicting compounds with low penetration." @default.
- W3088615466 created "2020-10-01" @default.
- W3088615466 creator A5041926767 @default.
- W3088615466 creator A5068512068 @default.
- W3088615466 creator A5080950722 @default.
- W3088615466 date "2020-12-01" @default.
- W3088615466 modified "2023-10-09" @default.
- W3088615466 title "A Recurrent Neural Network model to predict blood–brain barrier permeability" @default.
- W3088615466 cites W1899644141 @default.
- W3088615466 cites W1977960636 @default.
- W3088615466 cites W1990399577 @default.
- W3088615466 cites W1996950868 @default.
- W3088615466 cites W1999798000 @default.
- W3088615466 cites W2002878672 @default.
- W3088615466 cites W2016610112 @default.
- W3088615466 cites W2017688841 @default.
- W3088615466 cites W2022326777 @default.
- W3088615466 cites W2027582327 @default.
- W3088615466 cites W2030117383 @default.
- W3088615466 cites W2035669331 @default.
- W3088615466 cites W2043380313 @default.
- W3088615466 cites W2049334526 @default.
- W3088615466 cites W2064675550 @default.
- W3088615466 cites W2076063813 @default.
- W3088615466 cites W2076448934 @default.
- W3088615466 cites W2082185517 @default.
- W3088615466 cites W2087908817 @default.
- W3088615466 cites W2128255639 @default.
- W3088615466 cites W2148143831 @default.
- W3088615466 cites W2180697714 @default.
- W3088615466 cites W2310694886 @default.
- W3088615466 cites W2325735934 @default.
- W3088615466 cites W2340782621 @default.
- W3088615466 cites W2406943157 @default.
- W3088615466 cites W2467309505 @default.
- W3088615466 cites W2519019522 @default.
- W3088615466 cites W2720702472 @default.
- W3088615466 cites W2729085143 @default.
- W3088615466 cites W2790808809 @default.
- W3088615466 cites W2791713605 @default.
- W3088615466 cites W2801991413 @default.
- W3088615466 cites W2809595040 @default.
- W3088615466 cites W2887113605 @default.
- W3088615466 cites W2887710298 @default.
- W3088615466 cites W2901476322 @default.
- W3088615466 cites W2902620820 @default.
- W3088615466 cites W2959450640 @default.
- W3088615466 cites W2969570894 @default.
- W3088615466 cites W2969849947 @default.
- W3088615466 cites W2994728899 @default.
- W3088615466 cites W3018074893 @default.
- W3088615466 cites W3098269892 @default.
- W3088615466 cites W3104887532 @default.
- W3088615466 doi "https://doi.org/10.1016/j.compbiolchem.2020.107377" @default.
- W3088615466 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33010784" @default.
- W3088615466 hasPublicationYear "2020" @default.
- W3088615466 type Work @default.
- W3088615466 sameAs 3088615466 @default.
- W3088615466 citedByCount "34" @default.
- W3088615466 countsByYear W30886154662020 @default.
- W3088615466 countsByYear W30886154662021 @default.
- W3088615466 countsByYear W30886154662022 @default.
- W3088615466 countsByYear W30886154662023 @default.
- W3088615466 crossrefType "journal-article" @default.
- W3088615466 hasAuthorship W3088615466A5041926767 @default.
- W3088615466 hasAuthorship W3088615466A5068512068 @default.
- W3088615466 hasAuthorship W3088615466A5080950722 @default.
- W3088615466 hasBestOaLocation W30886154661 @default.
- W3088615466 hasConcept C108583219 @default.
- W3088615466 hasConcept C119857082 @default.
- W3088615466 hasConcept C147597530 @default.
- W3088615466 hasConcept C154945302 @default.
- W3088615466 hasConcept C185592680 @default.
- W3088615466 hasConcept C41008148 @default.
- W3088615466 hasConcept C50644808 @default.
- W3088615466 hasConcept C68762167 @default.
- W3088615466 hasConcept C95623464 @default.
- W3088615466 hasConceptScore W3088615466C108583219 @default.
- W3088615466 hasConceptScore W3088615466C119857082 @default.
- W3088615466 hasConceptScore W3088615466C147597530 @default.
- W3088615466 hasConceptScore W3088615466C154945302 @default.
- W3088615466 hasConceptScore W3088615466C185592680 @default.
- W3088615466 hasConceptScore W3088615466C41008148 @default.
- W3088615466 hasConceptScore W3088615466C50644808 @default.
- W3088615466 hasConceptScore W3088615466C68762167 @default.
- W3088615466 hasConceptScore W3088615466C95623464 @default.
- W3088615466 hasFunder F4320335726 @default.
- W3088615466 hasLocation W30886154661 @default.
- W3088615466 hasOpenAccess W3088615466 @default.
- W3088615466 hasPrimaryLocation W30886154661 @default.
- W3088615466 hasRelatedWork W2519019522 @default.
- W3088615466 hasRelatedWork W3014300295 @default.
- W3088615466 hasRelatedWork W3164822677 @default.
- W3088615466 hasRelatedWork W4223943233 @default.
- W3088615466 hasRelatedWork W4225161397 @default.
- W3088615466 hasRelatedWork W4312200629 @default.
- W3088615466 hasRelatedWork W4360585206 @default.
- W3088615466 hasRelatedWork W4364306694 @default.