Alexander Kroll

568 total citations · 1 hit paper
9 papers, 298 citations indexed

About

Alexander Kroll is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Alexander Kroll has authored 9 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 1 paper in Materials Chemistry. Recurrent topics in Alexander Kroll's work include Computational Drug Discovery Methods (8 papers), Protein Structure and Dynamics (7 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Alexander Kroll is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Protein Structure and Dynamics (7 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Alexander Kroll collaborates with scholars based in Germany, Sweden and India. Alexander Kroll's co-authors include Martin J. Lercher, Martin K. M. Engqvist, David Heckmann and Gregory Butler and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and PLoS Biology.

In The Last Decade

Alexander Kroll

7 papers receiving 292 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.

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

2023 95 citations Alexander Kroll, Martin J. Lercher 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
Alexander Kroll Germany	6	248	86	48	35	14	9	298
Jianan Canal Li United States	2	197 0.8×	36 0.4×	21 0.4×	30 0.9×	17 1.2×	3	257
Sergio Grimbs Germany	9	239 1.0×	73 0.8×	51 1.1×	14 0.4×	4 0.3×	15	307
Aashutosh Girish Boob United States	8	260 1.0×	25 0.3×	81 1.7×	18 0.5×	26 1.9×	11	320
Thomas Duigou France	7	360 1.5×	81 0.9×	87 1.8×	19 0.5×	11 0.8×	10	399
James G. Jeffryes United States	7	317 1.3×	53 0.6×	53 1.1×	32 0.9×	5 0.4×	8	357
Raine E. S. Thomson Australia	8	237 1.0×	20 0.2×	32 0.7×	45 1.3×	29 2.1×	12	303
Mark Ashton United Kingdom	7	118 0.5×	61 0.7×	40 0.8×	51 1.5×	4 0.3×	15	289
Abdelmoneim Amer Desouki Germany	4	276 1.1×	21 0.2×	90 1.9×	14 0.4×	4 0.3×	8	291
Maria‐Anna Trapotsi United Kingdom	8	133 0.5×	97 1.1×	16 0.3×	15 0.4×	1 0.1×	11	223
Paul S. Bond United Kingdom	8	106 0.4×	25 0.3×	15 0.3×	45 1.3×	6 0.4×	13	201

Countries citing papers authored by Alexander Kroll

Since Specialization

Citations

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

Fields of papers citing papers by Alexander Kroll

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexander Kroll

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Kroll. A scholar is included among the top collaborators of Alexander Kroll 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 Alexander Kroll. Alexander Kroll 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.

Kroll, Alexander, et al.. (2025). Recent advances and future trends for protein–small molecule interaction predictions with protein language models. Current Opinion in Structural Biology. 93. 103070–103070.

2.

Kroll, Alexander, et al.. (2025). DeepMolecules: a web server for predicting enzyme and transporter–small molecule interactions. Nucleic Acids Research. 53(W1). W213–W218. 3 indexed citations

3.

Kroll, Alexander, et al.. (2024). SPOT: A machine learning model that predicts specific substrates for transport proteins. PLoS Biology. 22(9). e3002807–e3002807. 7 indexed citations

4.

Kroll, Alexander, et al.. (2024). A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships. PLoS Computational Biology. 20(5). e1012100–e1012100. 13 indexed citations

5.

Kroll, Alexander. (2024). Transfer learning improves predictions of enzyme kinetic parameters. Chem Catalysis. 4(9). 101121–101121.

6.

Kroll, Alexander & Martin J. Lercher. (2024). DLKcat cannot predict meaningful kcat values for mutants and unfamiliar enzymes. Biology Methods and Protocols. 9(1). bpae061–bpae061. 11 indexed citations

7.

Kroll, Alexander, et al.. (2023). A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nature Communications. 14(1). 2787–2787. 95 indexed citations

8.

Kroll, Alexander, et al.. (2023). Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nature Communications. 14(1). 4139–4139. 95 indexed citations breakdown →

9.

Kroll, Alexander, Martin K. M. Engqvist, David Heckmann, & Martin J. Lercher. (2021). Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLoS Biology. 19(10). e3001402–e3001402. 74 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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