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Frank von Delft
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2020 – today
- 2024
- [j8]Anna Carbery, Martin Buttenschoen, Rachael Skyner, Frank von Delft, Charlotte M. Deane:
Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures. J. Cheminformatics 16(1): 32 (2024) - 2023
- [j7]Jack Scantlebury
, Lucy Vost
A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening. J. Chem. Inf. Model. 63(10): 2960-2974 (2023) - [j6]Stephanie Wills
Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search. J. Chem. Inf. Model. 63(11): 3423-3437 (2023) - 2022
- [j5]Harold Grosjean
SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction. J. Comput. Aided Mol. Des. 36(4): 291-311 (2022) - [j4]Simon Bray
Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease. J. Cheminformatics 14(1): 22 (2022) - [j3]Mihaela D. Smilova
Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins. J. Chem. Inf. Model. 62(2): 284-294 (2022) - 2020
- [j2]Jack Scantlebury
Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions. J. Chem. Inf. Model. 60(8): 3722-3730 (2020)
2010 – 2019
- 2015
- [j1]Anthony R. Bradley, Ian D. Wall, Frank von Delft
WONKA: objective novel complex analysis for ensembles of protein-ligand structures. J. Comput. Aided Mol. Des. 29(10): 963-973 (2015)
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