Daniel Reker

4.6k total citations · 2 hit papers
60 papers, 2.7k citations indexed

About

Daniel Reker is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Daniel Reker has authored 60 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 34 papers in Computational Theory and Mathematics and 15 papers in Pharmacology. Recurrent topics in Daniel Reker's work include Computational Drug Discovery Methods (34 papers), Microbial Natural Products and Biosynthesis (14 papers) and Machine Learning in Materials Science (12 papers). Daniel Reker is often cited by papers focused on Computational Drug Discovery Methods (34 papers), Microbial Natural Products and Biosynthesis (14 papers) and Machine Learning in Materials Science (12 papers). Daniel Reker collaborates with scholars based in United States, Switzerland and Germany. Daniel Reker's co-authors include Gisbert Schneider, Petra Schneider, Tiago Rodrigues, Gonçalo J. L. Bernardes, Michael Reutlinger, J.B. Brown, Giovanni Traverso, Jens Kunze, Emily Hoyt and Anna Maria Perna and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Angewandte Chemie International Edition and SHILAP Revista de lepidopterología.

In The Last Decade

Daniel Reker

59 papers receiving 2.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Counting on natural products for drug design

2016 930 citations Tiago Rodrigues, Daniel Reker et al. profile →
The landscape of small-molecule prodrugs

2024 61 citations Shaharyar M. Khan, Pei Zhou et al. Nature Reviews Drug Discovery profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Reker United States	25	1.4k	1.1k	576	509	434	60	2.7k
Jérôme Eberhardt United States	11	2.0k 1.4×	856 0.8×	371 0.6×	740 1.5×	276 0.6×	19	3.9k
Andreas F. Tillack United States	13	2.1k 1.5×	893 0.8×	371 0.6×	749 1.5×	527 1.2×	30	4.7k
Xuan-Yu Meng China	19	1.5k 1.1×	906 0.8×	269 0.5×	617 1.2×	270 0.6×	48	3.3k
Sorel Mureşan Sweden	23	1.6k 1.2×	1.7k 1.6×	522 0.9×	549 1.1×	483 1.1×	48	3.3k
Melissa F. Adasme Germany	11	1.9k 1.4×	1.1k 1.0×	369 0.6×	600 1.2×	272 0.6×	12	3.6k
Daniel Seeliger Germany	24	2.5k 1.8×	798 0.7×	241 0.4×	386 0.8×	475 1.1×	43	3.8k
Jin Huang China	34	2.5k 1.7×	1.2k 1.1×	328 0.6×	637 1.3×	365 0.8×	164	4.4k
Shuguang Yuan China	31	2.6k 1.8×	690 0.6×	276 0.5×	329 0.6×	242 0.6×	85	4.1k
Diogo Santos‐Martins United States	16	2.7k 1.9×	1.1k 1.0×	474 0.8×	1.0k 2.0×	349 0.8×	30	5.0k
V. Joachim Haupt Germany	19	2.1k 1.5×	1.1k 1.0×	381 0.7×	647 1.3×	238 0.5×	23	3.6k

Countries citing papers authored by Daniel Reker

Since Specialization

Citations

This map shows the geographic impact of Daniel Reker's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Reker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Reker more than expected).

Fields of papers citing papers by Daniel Reker

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Reker. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Reker. The network helps show where Daniel Reker may publish in the future.

Co-authorship network of co-authors of Daniel Reker

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Reker. A scholar is included among the top collaborators of Daniel Reker based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Reker. Daniel Reker is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Shi, Yunhua, Daniel Reker, James D. Byrne, et al.. (2024). Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning. Nature Biomedical Engineering. 8(3). 278–290. 3 indexed citations

Ha, Yuanchi, Helena Riuró, Feilun Wu, et al.. (2024). Data-driven learning of structure augments quantitative prediction of biological responses. PLoS Computational Biology. 20(6). e1012185–e1012185. 1 indexed citations

Khan, Shaharyar M., et al.. (2024). The landscape of small-molecule prodrugs. Nature Reviews Drug Discovery. 23(5). 365–380. 61 indexed citations breakdown →

Reker, Daniel, et al.. (2024). Finding the most potent compounds using active learning on molecular pairs. Beilstein Journal of Organic Chemistry. 20. 2152–2162. 1 indexed citations

Reker, Daniel, et al.. (2023). DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning. Journal of Cheminformatics. 15(1). 101–101. 22 indexed citations

Gardner, Apolonia, Aaron Lopes, Nhi V. Phan, et al.. (2023). Silk Fibroin-Based Coatings for Pancreatin-Dependent Drug Delivery. Journal of Pharmaceutical Sciences. 113(3). 718–724. 7 indexed citations

Wollborn, Jakob, Daniel Reker, Kai Kaufmann, et al.. (2021). Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection. Annals of Intensive Care. 11(1). 175–175. 20 indexed citations

Lin, Yen‐Chu, et al.. (2021). Combating small-molecule aggregation with machine learning. Cell Reports Physical Science. 2(9). 100573–100573. 12 indexed citations

Reker, Daniel. (2019). Cheminformatic Analysis of Natural Product Fragments. Fortschritte der Chemie Organischer Naturstoffe/Fortschritte der Chemie organischer Naturstoffe/Progress in the chemistry of organic natural products. 110. 143–175. 1 indexed citations

10.

Reker, Daniel. (2019). Practical considerations for active machine learning in drug discovery. Drug Discovery Today Technologies. 32-33. 73–79. 57 indexed citations

11.

Rakers, Christin, Daniel Reker, & J. Brown. (2017). Small Random Forest Models for Effective Chemogenomic Active Learning. 18(0). 124–142. 14 indexed citations

12.

Perna, Anna Maria, Tiago Rodrigues, Thomas P. Schmidt, et al.. (2015). Fragment‐Based De Novo Design Reveals a Small‐Molecule Inhibitor of Helicobacter Pylori HtrA. Angewandte Chemie International Edition. 54(35). 10244–10248. 41 indexed citations

13.

Reker, Daniel, Petra Schneider, Matthias Witschel, et al.. (2014). Deorphaning Pyrrolopyrazines as Potent Multi‐Target Antimalarial Agents. Angewandte Chemie International Edition. 53(27). 7079–7084. 28 indexed citations

14.

Reker, Daniel & Gisbert Schneider. (2014). Active-learning strategies in computer-assisted drug discovery. Drug Discovery Today. 20(4). 458–465. 177 indexed citations

15.

Schneider, Gisbert, Daniel Reker, Tiago Rodrigues, & Petra Schneider. (2014). Coping with Polypharmacology by Computational Medicinal Chemistry. CHIMIA International Journal for Chemistry. 68(9). 648–648. 4 indexed citations

16.

Siebert, Nikolai, et al.. (2013). Validated detection of anti-GD2 antibody ch14.18/CHO in serum of neuroblastoma patients using anti-idiotype antibody ganglidiomab. Journal of Immunological Methods. 398-399. 51–59. 12 indexed citations

17.

Lode, Holger N., Manuela Schmidt, Diana Seidel, et al.. (2013). Vaccination with anti-idiotype antibody ganglidiomab mediates a GD2-specific anti-neuroblastoma immune response. Cancer Immunology Immunotherapy. 62(6). 999–1010. 39 indexed citations

18.

Reutlinger, Michael, et al.. (2013). Chemically Advanced Template Search (CATS) for Scaffold‐Hopping and Prospective Target Prediction for ‘Orphan’ Molecules. Molecular Informatics. 32(2). 133–138. 133 indexed citations

19.

Lötsch, Jörn, Gisbert Schneider, Daniel Reker, et al.. (2013). Common non-epigenetic drugs as epigenetic modulators. Trends in Molecular Medicine. 19(12). 742–753. 58 indexed citations

20.

Fritz, Christian, et al.. (2010). Geospatial Web Mining for Emergency Management. MADOC (University of Mannheim). 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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