Martin Šícho

783 total citations
17 papers, 540 citations indexed

About

Martin Šícho is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Martin Šícho has authored 17 papers receiving a total of 540 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computational Theory and Mathematics, 9 papers in Molecular Biology and 6 papers in Materials Chemistry. Recurrent topics in Martin Šícho's work include Computational Drug Discovery Methods (15 papers), Machine Learning in Materials Science (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Martin Šícho is often cited by papers focused on Computational Drug Discovery Methods (15 papers), Machine Learning in Materials Science (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Martin Šícho collaborates with scholars based in Czechia, Germany and Netherlands. Martin Šícho's co-authors include Johannes Kirchmair, Conrad Stork, Christina de Bruyn Kops, Daniel Svozil, Angelica Mazzolari, Ya Chen, Alessandro Pedretti, Giulio Vistoli, Bernard Testa and Nina Jeliazkova and has published in prestigious journals such as Bioinformatics, Chemical Research in Toxicology and Bioorganic & Medicinal Chemistry.

In The Last Decade

Martin Šícho

15 papers receiving 533 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Martin Šícho Czechia 10 321 301 142 74 70 17 540
Conrad Stork Germany 12 303 0.9× 327 1.1× 127 0.9× 70 0.9× 63 0.9× 20 540
Christina de Bruyn Kops Germany 11 460 1.4× 396 1.3× 139 1.0× 117 1.6× 84 1.2× 15 718
Marlene T. Kim United States 13 249 0.8× 360 1.2× 120 0.8× 79 1.1× 44 0.6× 17 626
Wolfgang Muster Switzerland 13 220 0.7× 324 1.1× 75 0.5× 44 0.6× 81 1.2× 26 742
Kevin P. Cross United States 18 260 0.8× 343 1.1× 56 0.4× 43 0.6× 100 1.4× 41 735
Angelica Mazzolari Italy 16 451 1.4× 269 0.9× 103 0.7× 44 0.6× 58 0.8× 48 794
Sai Chetan K. Sukuru Switzerland 9 432 1.3× 471 1.6× 113 0.8× 81 1.1× 60 0.9× 11 686
Shuaishi Gao China 5 334 1.0× 296 1.0× 73 0.5× 74 1.0× 20 0.3× 5 574
Tomasz Magdziarz Poland 11 206 0.6× 291 1.0× 44 0.3× 59 0.8× 68 1.0× 34 524
Jens Kunze Switzerland 8 282 0.9× 220 0.7× 123 0.9× 36 0.5× 45 0.6× 9 572

Countries citing papers authored by Martin Šícho

Since Specialization
Citations

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

Fields of papers citing papers by Martin Šícho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Šícho

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

All Works

17 of 17 papers shown
1.
Yao, Yao, Natalia V. Ortiz Zacarı́as, Laura H. Heitman, et al.. (2025). Combining AlphaFold with Focused Virtual Library Design in the Development of Novel CCR2 and CCR5 Antagonists. Journal of Chemical Information and Modeling. 65(22). 12398–12409.
2.
Šícho, Martin, et al.. (2025). Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design. Journal of Cheminformatics. 17(1). 41–41. 5 indexed citations
3.
Šícho, Martin, et al.. (2025). TEMPL: A Template-Based Protein–Ligand Pose Prediction Baseline. Journal of Chemical Information and Modeling. 65(20). 11149–11157.
4.
Šícho, Martin, et al.. (2024). The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge. SLAS DISCOVERY. 29(2). 100144–100144. 11 indexed citations
5.
Šícho, Martin, et al.. (2024). AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models. Journal of Chemical Information and Modeling. 64(21). 8113–8122. 8 indexed citations
6.
Dehaen, Wim, Ya Chen, Johannes Kirchmair, et al.. (2024). Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library. Molecular Informatics. 43(8). e202300316–e202300316. 1 indexed citations
7.
Šícho, Martin, Linde Schoenmaker, Marina Gorostiola González, et al.. (2024). QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool. Journal of Cheminformatics. 16(1). 128–128. 4 indexed citations
8.
Šícho, Martin, et al.. (2023). DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space. Journal of Chemical Information and Modeling. 63(12). 3629–3636. 19 indexed citations
9.
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
10.
Kops, Christina de Bruyn, Martin Šícho, Angelica Mazzolari, & Johannes Kirchmair. (2020). GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics. Chemical Research in Toxicology. 34(2). 286–299. 98 indexed citations
11.
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
12.
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
13.
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
14.
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
15.
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
16.
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
17.
Šícho, Martin & Daniel Svozil. (2017). Molekulové dokování jako nástroj pro virtuální návrh léčiv. Chemické listy. 111(11). 754–759. 1 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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2026