Jochen Sieg

614 total citations
9 papers, 360 citations indexed

About

Jochen Sieg is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Jochen Sieg has authored 9 papers receiving a total of 360 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 8 papers in Molecular Biology and 4 papers in Computational Theory and Mathematics. Recurrent topics in Jochen Sieg's work include Protein Structure and Dynamics (6 papers), Enzyme Structure and Function (5 papers) and Machine Learning in Materials Science (4 papers). Jochen Sieg is often cited by papers focused on Protein Structure and Dynamics (6 papers), Enzyme Structure and Function (5 papers) and Machine Learning in Materials Science (4 papers). Jochen Sieg collaborates with scholars based in Germany. Jochen Sieg's co-authors include Matthias Rarey, Florian Flachsenberg, Katrin Stierand, Christiane Ehrt, Patrick Penner, Konrad Diedrich, Miriam Mathea, Andrea Volkamer, Christian Feldmann and Frederik Sandfort and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Proteins Structure Function and Bioinformatics.

In The Last Decade

Jochen Sieg

9 papers receiving 355 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jochen Sieg Germany	6	227	207	128	35	30	9	360
Avid M. Afzal United Kingdom	12	211 0.9×	247 1.2×	59 0.5×	25 0.7×	21 0.7×	20	352
Heval Ataş Türkiye	6	380 1.7×	317 1.5×	106 0.8×	33 0.9×	24 0.8×	10	556
Andreas Verras United States	10	248 1.1×	255 1.2×	117 0.9×	39 1.1×	51 1.7×	17	564
Manon Réau France	8	209 0.9×	163 0.8×	69 0.5×	23 0.7×	17 0.6×	11	318
Yanxing Wang China	10	221 1.0×	142 0.7×	73 0.6×	47 1.3×	43 1.4×	21	344
Yehor S. Malets Ukraine	2	281 1.2×	244 1.2×	88 0.7×	36 1.0×	47 1.6×	4	403
Andrew T. McNutt United States	6	312 1.4×	268 1.3×	113 0.9×	41 1.2×	48 1.6×	7	476
Nikolas Fechner Germany	11	218 1.0×	292 1.4×	115 0.9×	57 1.6×	26 0.9×	21	404
Xiaochu Tong China	7	164 0.7×	185 0.9×	82 0.6×	21 0.6×	17 0.6×	14	293
Brandon J. Bongers Netherlands	6	244 1.1×	266 1.3×	95 0.7×	43 1.2×	11 0.4×	8	343

Countries citing papers authored by Jochen Sieg

Since Specialization

Citations

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

Fields of papers citing papers by Jochen Sieg

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jochen Sieg

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

All Works

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9 of 9 papers shown

1.

Sieg, Jochen, et al.. (2024). Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years. Journal of Chemical Information and Modeling. 64(16). 6259–6280. 15 indexed citations

2.

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

3.

Feldmann, Christian, Jochen Sieg, & Miriam Mathea. (2024). Analysis of uncertainty of neural fingerprint-based models. Faraday Discussions. 256(0). 551–567. 1 indexed citations

4.

Sieg, Jochen & Matthias Rarey. (2023). Searching similar local 3D micro-environments in protein structure databases with MicroMiner. Briefings in Bioinformatics. 24(6). 5 indexed citations

5.

Sieg, Jochen, et al.. (2023). Modeling with Alternate Locations in X-ray Protein Structures. Journal of Chemical Information and Modeling. 63(8). 2573–2585. 3 indexed citations

6.

Sieg, Jochen, J. Lieske, Alke Meents, et al.. (2022). Analyzing structural features of proteins from deep‐sea organisms. Proteins Structure Function and Bioinformatics. 90(8). 1521–1537. 3 indexed citations

7.

Stierand, Katrin, Konrad Diedrich, Christiane Ehrt, et al.. (2022). ProteinsPlus: a comprehensive collection of web-based molecular modeling tools. Nucleic Acids Research. 50(W1). W611–W615. 112 indexed citations

8.

Sieg, Jochen, Florian Flachsenberg, & Matthias Rarey. (2019). In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening. Journal of Chemical Information and Modeling. 59(3). 947–961. 205 indexed citations

9.

Meyder, Agnes, et al.. (2018). StructureProfiler: an all-in-one tool for 3D protein structure profiling. Bioinformatics. 35(5). 874–876. 6 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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