Conrad Stork

832 total citations
20 papers, 540 citations indexed

About

Conrad Stork is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Conrad Stork has authored 20 papers receiving a total of 540 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computational Theory and Mathematics, 11 papers in Molecular Biology and 7 papers in Materials Chemistry. Recurrent topics in Conrad Stork's work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Conrad Stork is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Conrad Stork collaborates with scholars based in Germany, Norway and Austria. Conrad Stork's co-authors include Johannes Kirchmair, Martin Šícho, Christina de Bruyn Kops, Ya Chen, Daniel Svozil, Alessandro Pedretti, Giulio Vistoli, Bernard Testa, Angelica Mazzolari and Nina Jeliazkova and has published in prestigious journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Conrad Stork

19 papers receiving 532 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Conrad Stork Germany 12 327 303 127 70 70 20 540
Christina de Bruyn Kops Germany 11 396 1.2× 460 1.5× 139 1.1× 110 1.6× 117 1.7× 15 718
Martin Šícho Czechia 10 301 0.9× 321 1.1× 142 1.1× 46 0.7× 74 1.1× 17 540
Wolfgang Muster Switzerland 13 324 1.0× 220 0.7× 75 0.6× 65 0.9× 44 0.6× 26 742
Marlene T. Kim United States 13 360 1.1× 249 0.8× 120 0.9× 69 1.0× 79 1.1× 17 626
Arwa Bin Raies Saudi Arabia 9 340 1.0× 297 1.0× 49 0.4× 58 0.8× 119 1.7× 11 722
Tomasz Magdziarz Poland 11 291 0.9× 206 0.7× 44 0.3× 44 0.6× 59 0.8× 34 524
Iurii Sushko Germany 6 523 1.6× 265 0.9× 61 0.5× 62 0.9× 177 2.5× 13 709
Teresa Krieger-Burke United States 7 317 1.0× 356 1.2× 144 1.1× 62 0.9× 26 0.4× 11 788
Suman Chakravarti United States 12 278 0.9× 171 0.6× 37 0.3× 35 0.5× 70 1.0× 26 494
Emilio Xavier Esposito United States 13 438 1.3× 376 1.2× 30 0.2× 80 1.1× 137 2.0× 20 661

Countries citing papers authored by Conrad Stork

Since Specialization
Citations

This map shows the geographic impact of Conrad Stork'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 Conrad Stork with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Conrad Stork more than expected).

Fields of papers citing papers by Conrad Stork

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Conrad Stork. 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 Conrad Stork. The network helps show where Conrad Stork may publish in the future.

Co-authorship network of co-authors of Conrad Stork

This figure shows the co-authorship network connecting the top 25 collaborators of Conrad Stork. A scholar is included among the top collaborators of Conrad Stork 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 Conrad Stork. Conrad Stork is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kalita, Bhupalee, R.I. Zubatyuk, Dylan M. Anstine, et al.. (2025). AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry. Angewandte Chemie International Edition. 65(5). e16763–e16763.
2.
Tan, Lu, et al.. (2024). Tackling assay interference associated with small molecules. Nature Reviews Chemistry. 8(5). 319–339. 12 indexed citations
3.
Sieg, Jochen, Christian Feldmann, Jennifer Hemmerich, et al.. (2024). MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn. Journal of Chemical Information and Modeling. 64(24). 9027–9033. 10 indexed citations
4.
Stork, Conrad, et al.. (2023). Computational prediction of the metabolites of agrochemicals formed in rats. The Science of The Total Environment. 895. 165039–165039. 4 indexed citations
5.
6.
Stork, Conrad, et al.. (2021). CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. Bioorganic & Medicinal Chemistry. 46. 116388–116388. 34 indexed citations
7.
Stork, Conrad, et al.. (2021). Computational prediction of frequent hitters in target-based and cell-based assays. SHILAP Revista de lepidopterología. 1. 100007–100007. 5 indexed citations
8.
Kops, Christina de Bruyn, et al.. (2021). CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates. Molecules. 26(15). 4678–4678. 16 indexed citations
9.
Stork, Conrad, et al.. (2021). BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space. International Journal of Molecular Sciences. 22(15). 7773–7773. 4 indexed citations
10.
Norinder, Ulf, et al.. (2020). Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules. Chemical Research in Toxicology. 34(2). 330–344. 14 indexed citations
11.
Stork, Conrad, et al.. (2019). Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability. International Journal of Molecular Sciences. 20(19). 4833–4833. 19 indexed citations
12.
Bauer, Christoph, et al.. (2019). ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance. Molecular Informatics. 39(4). e1900103–e1900103. 6 indexed citations
13.
Stork, Conrad, Ya Chen, Martin Šícho, & Johannes Kirchmair. (2019). Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. Journal of Chemical Information and Modeling. 59(3). 1030–1043. 79 indexed citations
14.
Šícho, Martin, Conrad Stork, Angelica Mazzolari, et al.. (2019). FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes. Journal of Chemical Information and Modeling. 59(8). 3400–3412. 75 indexed citations
15.
16.
Kops, Christina de Bruyn, Conrad Stork, Martin Šícho, et al.. (2019). GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism. Frontiers in Chemistry. 7. 402–402. 55 indexed citations
17.
Stork, Conrad, Martin Šícho, Christina de Bruyn Kops, et al.. (2019). NERDD: a web portal providing access to in silico tools for drug discovery. Bioinformatics. 36(4). 1291–1292. 64 indexed citations
18.
Šícho, Martin, Christina de Bruyn Kops, Conrad Stork, Daniel Svozil, & Johannes Kirchmair. (2017). FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. Journal of Chemical Information and Modeling. 57(8). 1832–1846. 50 indexed citations
19.
Stork, Conrad, et al.. (2017). Hit Dexter: A Machine‐Learning Model for the Prediction of Frequent Hitters. ChemMedChem. 13(6). 564–571. 36 indexed citations
20.
Stork, Conrad, et al.. (2017). Why Are Dithienylethene‐Linked Biscobaltocenes so Hard to Photoswitch?. ChemPhysChem. 18(6). 596–609. 3 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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