Erinç Merdivan

618 total citations
12 papers, 178 citations indexed

About

Erinç Merdivan is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Erinç Merdivan has authored 12 papers receiving a total of 178 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Artificial Intelligence. Recurrent topics in Erinç Merdivan's work include Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers) and Machine Learning in Materials Science (2 papers). Erinç Merdivan is often cited by papers focused on Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers) and Machine Learning in Materials Science (2 papers). Erinç Merdivan collaborates with scholars based in Germany, Türkiye and Austria. Erinç Merdivan's co-authors include Serap Seyhan Bozkurt, Andreas Holzinger, Deepika Singh, Sten Hanke, Marie Piraud, Johannes Kropf, Alessandra Roncaglioni, Alberto Manganaro, Giuseppina Gini and Emilio Benfenati and has published in prestigious journals such as Nature Communications, Genome biology and PLoS Computational Biology.

In The Last Decade

Erinç Merdivan

11 papers receiving 175 citations

Peers

Erinç Merdivan

CTM

Manish K. Gupta India

Akihiro Yamamura Japan

Yatong Liu China

Xinhao Li China

Peisheng Cong China

Gustavo E. Vázquez Argentina

Ashwin Dhakal United States

W. Günther Germany

Yuanchao Xu China

Manish K. Gupta India

Erinç Merdivan

118 ×2.3

96 ×2.2

26 ×0.8

CTM

26 ×1.0

22 ×1.2

Citations per year, relative to Erinç Merdivan Erinç Merdivan (= 1×) peers Manish K. Gupta

Countries citing papers authored by Erinç Merdivan

Since Specialization

Citations

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

Fields of papers citing papers by Erinç Merdivan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Erinç Merdivan

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

All Works

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12 of 12 papers shown

Oestreich, Marie, Erinç Merdivan, Michael Lee, et al.. (2025). DrugDiff: small molecule diffusion model with flexible guidance towards molecular properties. Journal of Cheminformatics. 17(1). 23–23. 6 indexed citations

Merdivan, Erinç, et al.. (2025). TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor. Nature Communications. 16(1). 2737–2737. 4 indexed citations

Piraud, Marie, Stefan Kesselheim, Vincent Fortuin, et al.. (2025). OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders. PLoS Computational Biology. 21(11). e1013679–e1013679.

Yu, Yanying, et al.. (2024). Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration. Genome biology. 25(1). 13–13. 17 indexed citations

Singh, Deepika, Erinç Merdivan, Johannes Kropf, & Andreas Holzinger. (2024). Class imbalance in multi-resident activity recognition: an evaluative study on explainability of deep learning approaches. Universal Access in the Information Society. 24(2). 1173–1191. 11 indexed citations

Siebenmorgen, Till, Filipe Menezes, Erinç Merdivan, et al.. (2024). MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery. Nature Computational Science. 4(5). 367–378. 27 indexed citations

Merdivan, Erinç, Deepika Singh, Sten Hanke, et al.. (2020). Human Annotated Dialogues Dataset for Natural Conversational Agents. Applied Sciences. 10(3). 762–762. 13 indexed citations

Merdivan, Erinç, Deepika Singh, Sten Hanke, & Andreas Holzinger. (2019). Dialogue Systems for Intelligent Human Computer Interactions. Electronic Notes in Theoretical Computer Science. 343. 57–71. 24 indexed citations

Benfenati, Emilio, et al.. (2016). New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds. Journal of Environmental Science and Health Part C. 34(2). 97–113. 25 indexed citations

10.

Yanıkoğlu, Berrin, et al.. (2016). BeamECOC: A local search for the optimization of the ECOC matrix. View. 198–203. 8 indexed citations

11.

Bozkurt, Serap Seyhan, et al.. (2010). A fluorescent chemical sensor for ethanol determination in alcoholic beverages. Microchimica Acta. 168(1-2). 141–145. 35 indexed citations

12.

Merdivan, Erinç, et al.. (2008). Fluorimetric detection of boron by azomethine-H in micellar solution and sol–gel. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 71(5). 2045–2049. 8 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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