Miriam Mathea

2.9k total citations · 1 hit paper
20 papers, 1.5k citations indexed

About

Miriam Mathea is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Miriam Mathea has authored 20 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computational Theory and Mathematics, 8 papers in Molecular Biology and 8 papers in Materials Chemistry. Recurrent topics in Miriam Mathea's work include Computational Drug Discovery Methods (16 papers), Machine Learning in Materials Science (8 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Miriam Mathea is often cited by papers focused on Computational Drug Discovery Methods (16 papers), Machine Learning in Materials Science (8 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Miriam Mathea collaborates with scholars based in Germany, United States and Austria. Miriam Mathea's co-authors include Andrew Palmer, Philipp Eiden, Kevin Yang, Brian Kelley, Connor W. Coley, Volker Settels, Hua Gao, Wengong Jin, Regina Barzilay and Tommi Jaakkola and has published in prestigious journals such as Scientific Reports, Chemical Research in Toxicology and Risk Analysis.

In The Last Decade

Miriam Mathea

18 papers receiving 1.4k 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.

Analyzing Learned Molecular Representations for Property Prediction

2019 1.1k citations Kevin Yang, Kyle Swanson et al. Journal of Chemical Information and Modeling profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Miriam Mathea Germany	11	1.1k	803	662	129	92	20	1.5k
Philipp Eiden Germany	8	895 0.8×	788 1.0×	537 0.8×	97 0.8×	71 0.8×	9	1.2k
Dejun Jiang China	20	1.1k 1.0×	634 0.8×	914 1.4×	140 1.1×	62 0.7×	54	1.7k
Timur Madzhidov Russia	18	818 0.8×	662 0.8×	524 0.8×	75 0.6×	96 1.0×	60	1.2k
Zhaoping Xiong China	13	1.1k 1.0×	647 0.8×	828 1.3×	164 1.3×	55 0.6×	22	1.6k
Pavel Polishchuk Czechia	20	1.0k 1.0×	577 0.7×	652 1.0×	52 0.4×	124 1.3×	52	1.5k
Nikolaus Stiefl Switzerland	20	779 0.7×	393 0.5×	742 1.1×	104 0.8×	114 1.2×	43	1.5k
Feisheng Zhong China	13	1.1k 1.0×	671 0.8×	952 1.4×	123 1.0×	49 0.5×	18	1.7k
Jike Wang China	18	753 0.7×	458 0.6×	660 1.0×	68 0.5×	41 0.4×	52	1.1k
Youjun Xu China	13	873 0.8×	446 0.6×	849 1.3×	98 0.8×	75 0.8×	20	1.6k
Floriane Montanari Austria	17	801 0.7×	458 0.6×	587 0.9×	45 0.3×	87 0.9×	26	1.2k

Countries citing papers authored by Miriam Mathea

Since Specialization

Citations

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

Fields of papers citing papers by Miriam Mathea

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miriam Mathea

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

Mathea, Miriam, et al.. (2025). Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data. Journal of Chemical Information and Modeling. 65(19). 9871–9891.

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

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

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

Feldmann, Christian, et al.. (2024). Augmenting optimization-based molecular design with graph neural networks. Computers & Chemical Engineering. 186. 108684–108684. 5 indexed citations

Luijten, Mirjam, Jan van Benthem, Takeshi Morita, et al.. (2024). Evaluation of the standard battery of in vitro genotoxicity tests to predict in vivo genotoxicity through mathematical modeling: A report from the 8th International Workshop on Genotoxicity Testing. Environmental and Molecular Mutagenesis. 66(S2). 17–30. 2 indexed citations

Feldmann, Christian, et al.. (2023). Optimizing over trained GNNs via symmetry breaking. 44898–44924.

Svensson, Fredrik, et al.. (2022). Consideration of predicted small-molecule metabolites in computational toxicology. Digital Discovery. 1(2). 158–172. 12 indexed citations

Norinder, Ulf, et al.. (2022). Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data. Scientific Reports. 12(1). 7244–7244. 8 indexed citations

10.

Kolle, Susanne N., Miriam Mathea, Andreas Natsch, & Robert Landsiedel. (2021). Assessing Experimental Uncertainty in Defined Approaches: Borderline Ranges for In Chemico and In Vitro Skin Sensitization Methods Determined from Ring Trial Data. 7(3). 102–111. 9 indexed citations

11.

Norinder, Ulf, Roland Buesen, Robert Landsiedel, et al.. (2021). ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities. Journal of Chemical Information and Modeling. 61(7). 3255–3272. 24 indexed citations

12.

Kolle, Susanne N., Miriam Mathea, Andreas Natsch, & Robert Landsiedel. (2021). Borderline ranges for in chemico and in vitro skin sensitization methods determined from ring trial data - acknowledging experimental uncertainty in defined approaches. Toxicology Letters. 350. S93–S93. 1 indexed citations

13.

Mathea, Miriam, Janosch Achenbach, Antje Wolf, et al.. (2020). KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development. Journal of Cheminformatics. 12(1). 24–24. 19 indexed citations

14.

Ball, Nicholas, Judith C. Madden, Alicia Paini, et al.. (2020). Key read across framework components and biology based improvements. Mutation Research/Genetic Toxicology and Environmental Mutagenesis. 853. 503172–503172. 16 indexed citations

15.

Birk, Barbara, et al.. (2020). In Silico Models to Predict the Perturbation of Molecular Initiating Events Related to Thyroid Hormone Homeostasis. Chemical Research in Toxicology. 34(2). 396–411. 39 indexed citations

16.

Gabbert, Silke, Miriam Mathea, Susanne N. Kolle, & Robert Landsiedel. (2020). Accounting for Precision Uncertainty of Toxicity Testing: Methods to Define Borderline Ranges and Implications for Hazard Assessment of Chemicals. Risk Analysis. 42(2). 224–238. 10 indexed citations

17.

Yang, Kevin, Kyle Swanson, Wengong Jin, et al.. (2019). Correction to Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling. 59(12). 5304–5305. 25 indexed citations

18.

Yang, Kevin, Kyle Swanson, Wengong Jin, et al.. (2019). Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling. 59(8). 3370–3388. 1081 indexed citations breakdown →

19.

Mathea, Miriam, et al.. (2017). Efficiency of different measures for defining the applicability domain of classification models. Journal of Cheminformatics. 9(1). 44–44. 58 indexed citations

20.

Mathea, Miriam, et al.. (2016). Chemoinformatic Classification Methods and their Applicability Domain. Molecular Informatics. 35(5). 160–180. 116 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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