Hochschulschrift
Development and bioanalytical application of affinity-mass spectrometry for identification and structural characterisation of protein-ligand interactions
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Konstanz, Universität Konstanz, Diss., 2013
- Klassifikation
-
Chemie
- Ereignis
-
Veröffentlichung
- (wo)
-
Konstanz
- (wer)
-
Bibliothek der Universität Konstanz
- (wann)
-
2014
- Urheber
-
Stumbaum, Mihaela
- URN
-
urn:nbn:de:bsz:352-265034
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:50 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.
Objekttyp
- Hochschulschrift
Beteiligte
- Stumbaum, Mihaela
- Bibliothek der Universität Konstanz
Entstanden
- 2014
Ähnliche Objekte (12)
Ensembling methods for protein-ligand binding affinity prediction
Learning protein-ligand binding affinity with atomic environment vectors
Protein-ligand interactions
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Information theory based scoring function for predicting protein-ligand binding affinity
Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3
Interformer: an interaction-aware model for protein-ligand docking and affinity prediction
Investigation of Protein-Ligand Complexes by Native Mass Spectrometry and Ion Mobility-Mass Spectrometry
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Interrogating protein-ligand interactions through structural mass spectrometry, computational simulations and biophysical techniques
Functional dynamics of protein-ligand interactions
Ensembling methods for protein-ligand binding affinity prediction
Learning protein-ligand binding affinity with atomic environment vectors
Protein-ligand interactions
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Information theory based scoring function for predicting protein-ligand binding affinity
Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3
Interformer: an interaction-aware model for protein-ligand docking and affinity prediction
Investigation of Protein-Ligand Complexes by Native Mass Spectrometry and Ion Mobility-Mass Spectrometry
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Interrogating protein-ligand interactions through structural mass spectrometry, computational simulations and biophysical techniques
Functional dynamics of protein-ligand interactions
Ensembling methods for protein-ligand binding affinity prediction
Learning protein-ligand binding affinity with atomic environment vectors
Protein-ligand interactions
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Information theory based scoring function for predicting protein-ligand binding affinity
Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3
Interformer: an interaction-aware model for protein-ligand docking and affinity prediction
Investigation of Protein-Ligand Complexes by Native Mass Spectrometry and Ion Mobility-Mass Spectrometry
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Interrogating protein-ligand interactions through structural mass spectrometry, computational simulations and biophysical techniques