FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2898867502> ?p ?o ?g. }

• W2898867502 endingPage "14716" @default.
• W2898867502 startingPage "14704" @default.
• W2898867502 abstract "Machine learning methods have revolutionized modern science, providing fast and accurate solutions to multiple problems. However, they are commonly treated as “black boxes”. Therefore, in important scientific fields such as medicinal chemistry and drug discovery, machine learning methods are restricted almost exclusively to the task of performing predictions of large and heterogeneous data sets of chemicals. The lack of interpretability prevents the full exploitation of the machine learning models as generators of new chemical knowledge. This work focuses on the development of an ensemble learning model for the prediction and design of potent dual heat shock protein 90 (Hsp90) inhibitors. The model displays accuracy higher than 80% in both training and test sets. To use the ensemble model as a generator of new chemical knowledge, three steps were followed. First, a physicochemical and/or structural interpretation was provided for each molecular descriptor present in the ensemble learning model. Second, the term “pseudolinear equation” was introduced within the context of machine learning to calculate the relative quantitative contributions of different molecular fragments to the inhibitory activity against the two Hsp90 isoforms studied here. Finally, by assembling the fragments with positive contributions, new molecules were designed, being predicted as potent Hsp90 inhibitors. According to Lipinski’s rule of five, the designed molecules were found to exhibit potentially good oral bioavailability, a primordial property that chemicals must have to pass early stages in drug discovery. The present approach based on the combination of ensemble learning and fragment-based topological design holds great promise in drug discovery, and it can be adapted and applied to many different scientific disciplines." @default.
• W2898867502 created "2018-11-09" @default.
• W2898867502 creator A5077424712 @default.
• W2898867502 date "2018-11-02" @default.
• W2898867502 modified "2023-10-08" @default.
• W2898867502 title "Combining Ensemble Learning with a Fragment-Based Topological Approach To Generate New Molecular Diversity in Drug Discovery: In Silico Design of Hsp90 Inhibitors" @default.
• W2898867502 cites W1560940583 @default.
• W2898867502 cites W1970698820 @default.
• W2898867502 cites W1975447903 @default.
• W2898867502 cites W1996405164 @default.
• W2898867502 cites W2013504674 @default.
• W2898867502 cites W2025681444 @default.
• W2898867502 cites W2026355982 @default.
• W2898867502 cites W2032703161 @default.
• W2898867502 cites W2036887984 @default.
• W2898867502 cites W2039828250 @default.
• W2898867502 cites W2040494696 @default.
• W2898867502 cites W2048816044 @default.
• W2898867502 cites W2053717624 @default.
• W2898867502 cites W2063956958 @default.
• W2898867502 cites W2079494052 @default.
• W2898867502 cites W2096541451 @default.
• W2898867502 cites W2099071242 @default.
• W2898867502 cites W2102395554 @default.
• W2898867502 cites W2105649494 @default.
• W2898867502 cites W2107680276 @default.
• W2898867502 cites W2109553965 @default.
• W2898867502 cites W2132057369 @default.
• W2898867502 cites W2135732933 @default.
• W2898867502 cites W2137226045 @default.
• W2898867502 cites W2143940733 @default.
• W2898867502 cites W2176516200 @default.
• W2898867502 cites W2264671791 @default.
• W2898867502 cites W2267537937 @default.
• W2898867502 cites W2269909407 @default.
• W2898867502 cites W2319086271 @default.
• W2898867502 cites W2414912995 @default.
• W2898867502 cites W2418882863 @default.
• W2898867502 cites W2578240541 @default.
• W2898867502 cites W2588136288 @default.
• W2898867502 cites W2609261056 @default.
• W2898867502 cites W2612060048 @default.
• W2898867502 cites W2622826845 @default.
• W2898867502 cites W2623060321 @default.
• W2898867502 cites W2623671274 @default.
• W2898867502 cites W2736101782 @default.
• W2898867502 cites W2736137960 @default.
• W2898867502 cites W2753588101 @default.
• W2898867502 cites W2755651226 @default.
• W2898867502 cites W2779266190 @default.
• W2898867502 cites W2785372418 @default.
• W2898867502 cites W2801991413 @default.
• W2898867502 cites W2804288893 @default.
• W2898867502 cites W2805002767 @default.
• W2898867502 cites W2805903905 @default.
• W2898867502 cites W2807921307 @default.
• W2898867502 cites W2888453613 @default.
• W2898867502 cites W2890240722 @default.
• W2898867502 cites W3098269892 @default.
• W2898867502 cites W4213151958 @default.
• W2898867502 cites W4253059622 @default.
• W2898867502 doi "https://doi.org/10.1021/acsomega.8b02419" @default.
• W2898867502 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6289491" @default.
• W2898867502 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30555986" @default.
• W2898867502 hasPublicationYear "2018" @default.
• W2898867502 type Work @default.
• W2898867502 sameAs 2898867502 @default.
• W2898867502 citedByCount "25" @default.
• W2898867502 countsByYear W28988675022019 @default.
• W2898867502 countsByYear W28988675022020 @default.
• W2898867502 countsByYear W28988675022021 @default.
• W2898867502 countsByYear W28988675022022 @default.
• W2898867502 crossrefType "journal-article" @default.
• W2898867502 hasAuthorship W2898867502A5077424712 @default.
• W2898867502 hasBestOaLocation W28988675021 @default.
• W2898867502 hasConcept C104317684 @default.
• W2898867502 hasConcept C11413529 @default.
• W2898867502 hasConcept C119857082 @default.
• W2898867502 hasConcept C151730666 @default.
• W2898867502 hasConcept C154945302 @default.
• W2898867502 hasConcept C185592680 @default.
• W2898867502 hasConcept C2775905019 @default.
• W2898867502 hasConcept C2776235265 @default.
• W2898867502 hasConcept C2779343474 @default.
• W2898867502 hasConcept C2781067378 @default.
• W2898867502 hasConcept C41008148 @default.
• W2898867502 hasConcept C45942800 @default.
• W2898867502 hasConcept C55493867 @default.
• W2898867502 hasConcept C60644358 @default.
• W2898867502 hasConcept C70721500 @default.
• W2898867502 hasConcept C74187038 @default.
• W2898867502 hasConcept C86803240 @default.
• W2898867502 hasConcept C99726746 @default.
• W2898867502 hasConceptScore W2898867502C104317684 @default.
• W2898867502 hasConceptScore W2898867502C11413529 @default.
• W2898867502 hasConceptScore W2898867502C119857082 @default.
• W2898867502 hasConceptScore W2898867502C151730666 @default.
• W2898867502 hasConceptScore W2898867502C154945302 @default.

• next