Daniel Reker
Daniel Reker
Assistant Professor at Duke University
Verified email at - Homepage
Cited by
Cited by
Counting on natural products for drug design
T Rodrigues, D Reker, P Schneider, G Schneider
Nature chemistry 8 (6), 531-541, 2016
Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus
D Reker, T Rodrigues, P Schneider, G Schneider
Proceedings of the National Academy of Sciences 111 (11), 4067-4072, 2014
Active-learning strategies in computer-assisted drug discovery
D Reker, G Schneider
Drug discovery today 20 (4), 458-465, 2015
Chemically Advanced Template Search (CATS) for Scaffold‐Hopping and Prospective Target Prediction for ‘Orphan’Molecules
M Reutlinger, CP Koch, D Reker, N Todoroff, P Schneider, T Rodrigues, ...
Molecular Informatics 32 (2), 133-138, 2013
Revealing the macromolecular targets of complex natural products
D Reker, AM Perna, T Rodrigues, P Schneider, M Reutlinger, B Mönch, ...
Nature Chemistry 6 (12), 1072-1078, 2014
Artificial intelligence in chemistry and drug design
N Brown, P Ertl, R Lewis, T Luksch, D Reker, N Schneider
Journal of Computer-Aided Molecular Design 34 (7), 709-715, 2020
“Inactive” ingredients in oral medications
D Reker, SM Blum, C Steiger, KE Anger, JM Sommer, J Fanikos, ...
Science Translational Medicine 11 (483), eaau6753, 2019
Active learning for computational chemogenomics
D Reker, P Schneider, G Schneider, JB Brown
Future Medicinal Chemistry 9 (4), 381-402, 2017
Common non-epigenetic drugs as epigenetic modulators
J Lötsch, G Schneider, D Reker, MJ Parnham, P Schneider, G Geisslinger, ...
Trends in molecular medicine 19 (12), 742-753, 2013
Computationally guided high-throughput design of self-assembling drug nanoparticles
D Reker, Y Rybakova, AR Kirtane, R Cao, JW Yang, N Navamajiti, ...
Nature nanotechnology 16 (6), 725-733, 2021
Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors
D Reker, P Schneider, G Schneider
Chemical Science 7 (6), 3919-3927, 2016
Practical considerations for active machine learning in drug discovery
D Reker
Drug Discovery Today: Technologies 32, 73-79, 2019
Artificial intelligence for natural product drug discovery
MW Mullowney, KR Duncan, SS Elsayed, N Garg, JJJ van der Hooft, ...
Nature Reviews Drug Discovery 22 (11), 895-916, 2023
Computational advances in combating colloidal aggregation in drug discovery
D Reker, GJL Bernardes, T Rodrigues
Nature chemistry 11 (5), 402-418, 2019
Adaptive optimization of chemical reactions with minimal experimental information
D Reker, EA Hoyt, GJL Bernardes, T Rodrigues
Cell Reports Physical Science 1 (11), 100247, 2020
Revealing the Macromolecular Targets of Fragment‐Like Natural Products
T Rodrigues, D Reker, J Kunze, P Schneider, G Schneider
Angewandte Chemie International Edition 54 (36), 10516-10520, 2015
Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections
A Abramson, AR Kirtane, Y Shi, G Zhong, JE Collins, S Tamang, K Ishida, ...
Matter 5 (3), 975-987, 2022
Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees
L Li, CC Koh, D Reker, JB Brown, H Wang, NK Lee, H Liow, H Dai, ...
Scientific reports 9 (1), 1-12, 2019
Fragment‐Based De Novo Design Reveals a Small‐Molecule Inhibitor of Helicobacter Pylori HtrA
AM Perna, T Rodrigues, TP Schmidt, M Böhm, K Stutz, D Reker, B Pfeiffer, ...
Angewandte Chemie International Edition 54 (35), 10244-10248, 2015
Multidimensional De Novo Design Reveals 5‐HT2B Receptor‐Selective Ligands
T Rodrigues, N Hauser, D Reker, M Reutlinger, T Wunderlin, J Hamon, ...
Angewandte Chemie International Edition 54 (5), 1551-1555, 2015
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