Frank von Delft
researcher
Facts
5 properties- Instance of
- human
- Occupation
- researcher, performing artist
🏷️ Also known as
en Frank von Delft
en von Delft F
🔖 Identifiers · 5
- ORCID iD
- 0000-0003-0378-0017
- Flanders Arts Institute person ID (former scheme)
- 1914734
- Gateway to Research person ID
- 589898FD-1A93-407A-A416-AD9F76F58443
- Kunstenpunt Knowledge Graph ID
- Walter Frank
- Scopus author ID
- 7004734200
🔗 Connections
Author of 7
A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening,
CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritisation,
Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions,
Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries,
Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search,
Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures,
Proper modelling of ligand binding requires an ensemble of bound and unbound states
← Authored by 7
A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening,
CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritisation,
Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions,
Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries,
Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search,
Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures,
Proper modelling of ligand binding requires an ensemble of bound and unbound states
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