Djork-Arné Clevert

7.0k total citations · 3 hit papers
38 papers, 2.4k citations indexed

About

Djork-Arné Clevert is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Djork-Arné Clevert has authored 38 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 21 papers in Computational Theory and Mathematics and 16 papers in Materials Chemistry. Recurrent topics in Djork-Arné Clevert's work include Computational Drug Discovery Methods (20 papers), Machine Learning in Materials Science (14 papers) and Gene expression and cancer classification (10 papers). Djork-Arné Clevert is often cited by papers focused on Computational Drug Discovery Methods (20 papers), Machine Learning in Materials Science (14 papers) and Gene expression and cancer classification (10 papers). Djork-Arné Clevert collaborates with scholars based in Germany, Austria and United States. Djork-Arné Clevert's co-authors include Sepp Hochreiter, Floriane Montanari, Robin Winter, Frank Noé, Andreas Mayr, Günter Klambauer, Andreas Mitterecker, Klaus Obermayer, Ulrich Bodenhofer and Joerg Wichard and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Djork-Arné Clevert

37 papers receiving 2.4k 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.

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

2018 352 citations Andreas Mayr, Thomas Unterthiner et al. Chemical Science profile →
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

2018 326 citations Robin Winter, Floriane Montanari et al. Chemical Science profile →
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

2020 283 citations Oscar Méndez‐Lucio, Benoît Baillif et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Djork-Arné Clevert Germany	18	1.5k	1.1k	765	299	197	38	2.4k
Günter Klambauer Austria	24	1.5k 1.0×	1.6k 1.4×	758 1.0×	287 1.0×	308 1.6×	45	3.1k
Andreas Mayr Austria	13	1.0k 0.7×	879 0.8×	444 0.6×	258 0.9×	262 1.3×	17	1.9k
Brian Kelley United States	25	1.9k 1.3×	1.5k 1.3×	877 1.1×	228 0.8×	157 0.8×	61	3.6k
Michaela Spitzer United Kingdom	13	1.4k 0.9×	962 0.8×	447 0.6×	176 0.6×	218 1.1×	17	2.8k
Shanrong Zhao United States	19	2.1k 1.4×	866 0.8×	450 0.6×	251 0.8×	232 1.2×	35	3.7k
Hugo Ceulemans Belgium	30	3.0k 2.0×	789 0.7×	557 0.7×	235 0.8×	105 0.5×	59	4.4k
Parantu K. Shah United States	19	1.8k 1.2×	851 0.7×	424 0.6×	130 0.4×	391 2.0×	42	3.1k
Jianyang Zeng China	28	2.8k 1.9×	1.4k 1.2×	469 0.6×	147 0.5×	307 1.6×	78	3.5k
Isidro Cortés‐Ciriano United Kingdom	26	1.3k 0.9×	874 0.8×	329 0.4×	169 0.6×	126 0.6×	63	2.3k
Jessica Vamathevan United Kingdom	11	1.1k 0.7×	845 0.7×	419 0.5×	90 0.3×	214 1.1×	18	2.3k

Countries citing papers authored by Djork-Arné Clevert

Since Specialization

Citations

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

Fields of papers citing papers by Djork-Arné Clevert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Djork-Arné Clevert. 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 Djork-Arné Clevert. The network helps show where Djork-Arné Clevert may publish in the future.

Co-authorship network of co-authors of Djork-Arné Clevert

This figure shows the co-authorship network connecting the top 25 collaborators of Djork-Arné Clevert. A scholar is included among the top collaborators of Djork-Arné Clevert 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 Djork-Arné Clevert. Djork-Arné Clevert 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

Ash, Jeremy R., Raquel Rodríguez-Pérez, Matteo Aldeghi, et al.. (2025). Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery. Journal of Chemical Information and Modeling. 65(18). 9398–9411. 6 indexed citations

Le, Tuan, et al.. (2025). Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning. Journal of Cheminformatics. 17(1). 90–90.

Kannas, Christos, et al.. (2025). Multi-objective synthesis planning by means of Monte Carlo Tree search. SHILAP Revista de lepidopterología. 7. 100130–100130. 1 indexed citations

Le, Tuan, et al.. (2024). PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling. Chemical Science. 15(36). 14954–14967. 10 indexed citations

Ash, Jeremy R., Matteo Aldeghi, Raquel Rodríguez-Pérez, et al.. (2024). A call for an industry-led initiative to critically assess machine learning for real-world drug discovery. Nature Machine Intelligence. 6(10). 1120–1121. 6 indexed citations

Genheden, Samuel, et al.. (2024). Models Matter: the impact of single-step retrosynthesis on synthesis planning. Digital Discovery. 3(3). 558–572. 16 indexed citations

Wong, Daniel R., et al.. (2023). Deep representation learning determines drug mechanism of action from cell painting images. Digital Discovery. 2(5). 1354–1367. 8 indexed citations

Wand, Michael, et al.. (2023). Reagent prediction with a molecular transformer improves reaction data quality. Chemical Science. 14(12). 3235–3246. 21 indexed citations

Méndez‐Lucio, Oscar, Tuan Le, Carsten Jörn Beese, et al.. (2022). Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features. Digital Discovery. 2(1). 91–102. 12 indexed citations

10.

Winter, Robin, et al.. (2021). Unsupervised Representation Learning for Proteochemometric Modeling. International Journal of Molecular Sciences. 22(23). 12882–12882. 8 indexed citations

11.

Clevert, Djork-Arné, Tuan Le, Robin Winter, & Floriane Montanari. (2021). Img2Mol – accurate SMILES recognition from molecular graphical depictions. Chemical Science. 12(42). 14174–14181. 38 indexed citations

12.

Le, Tuan, Robin Winter, Frank Noé, & Djork-Arné Clevert. (2020). Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures. Chemical Science. 11(38). 10378–10389. 40 indexed citations

13.

Méndez‐Lucio, Oscar, Benoît Baillif, Djork-Arné Clevert, David Rouquié, & Joerg Wichard. (2020). De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nature Communications. 11(1). 10–10. 283 indexed citations breakdown →

14.

Winter, Robin, Floriane Montanari, Andreas Steffen, et al.. (2019). Efficient multi-objective molecular optimization in a continuous latent space. Chemical Science. 10(34). 8016–8024. 156 indexed citations

15.

Montanari, Floriane, Lara Kuhnke, Antonius ter Laak, & Djork-Arné Clevert. (2019). Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks. Molecules. 25(1). 44–44. 70 indexed citations

16.

Clevert, Djork-Arné, et al.. (2018). PAVOOC: designing CRISPR sgRNAs using 3D protein structures and functional domain annotations. Bioinformatics. 35(13). 2309–2310. 7 indexed citations

17.

Winter, Robin, Floriane Montanari, Frank Noé, & Djork-Arné Clevert. (2018). Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chemical Science. 10(6). 1692–1701. 326 indexed citations breakdown →

18.

Clevert, Djork-Arné, Andreas Mayr, Thomas Unterthiner, & Sepp Hochreiter. (2015). Rectified factor networks. Neural Information Processing Systems. 28. 1855–1863. 2 indexed citations

19.

Kasim, Adetayo, Dan Lin, Suzy Van Sanden, et al.. (2010). Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach. Statistical Applications in Genetics and Molecular Biology. 9(1). Article 4–Article 4. 12 indexed citations

20.

Tuefferd, Marianne, An De Bondt, Ilse Van den Wyngaert, et al.. (2008). Genome‐wide copy number alterations detection in fresh frozen and matched FFPE samples using SNP 6.0 arrays. Genes Chromosomes and Cancer. 47(11). 957–964. 40 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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