Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling ; volume:15 ; number:1 ; day:13 ; month:6 ; year:2024 ; pages:1-13 ; date:12.2024
Nature Communications ; 15, Heft 1 (13.6.2024), 1-13, 12.2024
- Urheber
-
Zhang, Ruibo
Nolte, Daniel
Sanchez-Villalobos, Cesar
Ghosh, Souparno
Pal, Ranadip
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s41467-024-49372-0
- URN
-
urn:nbn:de:101:1-2408200853162.984704057075
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:52 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Zhang, Ruibo
- Nolte, Daniel
- Sanchez-Villalobos, Cesar
- Ghosh, Souparno
- Pal, Ranadip
- SpringerLink (Online service)
Ähnliche Objekte (12)
Interpretable correlation descriptors for quantitative structure-activity relationships
Interpretable scientific discovery with symbolic regression: a review
A practical overview of quantitative structure-activity relationship
Exploring the Structure–Activity Relationship on Platinum Nanoparticles
Structure‐Activity Relationship of Phenylpyrazolones against Trypanosoma cruzi
Multi-faceted structure-activity relationship analysis using graphical representations
Multi-faceted Structure-Activity Relationship Analysis Using Graphical Representations
Automatic generation of interpretable hyperelastic material models by symbolic regression
Computational methods for structure-activity relationship analysis and activity prediction
Structure‐Activity Relationship of NF023 Derivatives Binding to XIAP‐BIR1
Computational Methods for Structure-Activity Relationship Analysis and Activity Prediction
Quantitative Structure‐activity Relationship (QSAR) Models for Docking Score Correction
Interpretable correlation descriptors for quantitative structure-activity relationships
Interpretable scientific discovery with symbolic regression: a review
A practical overview of quantitative structure-activity relationship
Exploring the Structure–Activity Relationship on Platinum Nanoparticles
Structure‐Activity Relationship of Phenylpyrazolones against Trypanosoma cruzi
Multi-faceted structure-activity relationship analysis using graphical representations
Multi-faceted Structure-Activity Relationship Analysis Using Graphical Representations
Automatic generation of interpretable hyperelastic material models by symbolic regression
Computational methods for structure-activity relationship analysis and activity prediction
Structure‐Activity Relationship of NF023 Derivatives Binding to XIAP‐BIR1
Computational Methods for Structure-Activity Relationship Analysis and Activity Prediction
Quantitative Structure‐activity Relationship (QSAR) Models for Docking Score Correction
Interpretable correlation descriptors for quantitative structure-activity relationships
Interpretable scientific discovery with symbolic regression: a review
A practical overview of quantitative structure-activity relationship
Exploring the Structure–Activity Relationship on Platinum Nanoparticles
Structure‐Activity Relationship of Phenylpyrazolones against Trypanosoma cruzi
Multi-faceted structure-activity relationship analysis using graphical representations
Multi-faceted Structure-Activity Relationship Analysis Using Graphical Representations
Automatic generation of interpretable hyperelastic material models by symbolic regression
Computational methods for structure-activity relationship analysis and activity prediction
Structure‐Activity Relationship of NF023 Derivatives Binding to XIAP‐BIR1
Computational Methods for Structure-Activity Relationship Analysis and Activity Prediction