Paula Linh Kramer

Paula Linh Kramer

Former Member

PhD Student

Modeling and Simulation Group, Saarland University

Universität des Saarlandes

Campus E1 1, Room 1.18

66123 Saarbrücken

Germany

About Me

I am a PhD student at the chair of Modeling and Simulation. I am working as part of the NEDD research program, where I am applying machine learning approaches such as Graph Neural Networks to improve drug discovery. I am also interested in the statistical modeling of DNA methylation dynamics.

Publications

  • C. Kyriakopoulos, K. Nordström, P. Kramer, J. Gottfreund, A. Salhab, J. Arand, F. Müller, F. von Meyenn, G. Ficz, W. Reik, V. Wolf, J. Walter, P. Giehr: A comprehensive approach for genome-wide efficiency profiling of DNA modifying enzymes. Cell Report Methods. 2022. (pre-print)

Publications (6)

2026

2 papers

Evaluation beyond goodness of fit: Quantifying biophysical alignment of AI models for kinase-centric drug discovery

2026 — Artificial Intelligence in the Life Sciences (AILSCI)

@article{grossEvaluationBeyondGoodnessOf2026,
 author = {Joschka Groß and Michael Backenköhler and Paula Linh Kramer and Raquel López-Ríos De Castro and Guillermo Pérez-Hernández and Verena Wolf and Andrea Volkamer},
 doi = {10.1016/j.ailsci.2026.100169},
 journal = {Artificial Intelligence in the Life Sciences (AILSCI)},
 month = {6},
 number = {article 100169},
 pages = {1-17},
 publisher = {Elsevier},
 title = {Evaluation beyond goodness of fit: Quantifying biophysical alignment of AI models for kinase-centric drug discovery},
 url = {https://www.sciencedirect.com/science/article/pii/S2667318526000176?via%3Dihub},
 volume = {9},
 year = {2026}
}
DOI URL

Customkinfraglib: Filtering the kinase-focused fragmentation library

2026 — ACS omega

@article{linhkramerCustomkinfraglibFilteringTheKinaseFocused2026,
 author = {Linh Kramer, Paula and Buchthal, Katharina and Sydow, Dominique and Sonja Leo, Katharina and Volkamer, Andrea},
 doi = {10.1021/acsomega.5c12231},
 eprint = {https://pubs.acs.org/acsodf/article-pdf/11/10/16369/63264601/ao5c12231.pdf},
 issn = {2470-1343},
 journal = {ACS omega},
 month = {03},
 number = {10},
 pages = {16369--16380},
 publisher = {ACS Publications},
 title = {Customkinfraglib: Filtering the kinase-focused fragmentation library},
 url = {https://doi.org/10.1021/acsomega.5c12231},
 volume = {11},
 year = {2026}
}
DOI URL

2025

1 paper

The Journey of Data: Lessons Learned in Modeling Kinase Affinity, Selectivity, and Resistance [Article v1. 0]

2025 — Living Journal of Computational Molecular Science (LiveCoMS)

PDF
@article{castroTheJourneyOfData2025,
 author = {Raquel López-Ríos de Castro and Jaime Rodríguez-Guerra and David Schaller and Talia K Kimber and Corey Taylor and Jessica B White and Michael Backenköhler and Joschka Groß and Alexander M Payne and Benjamin Kaminow and Ivan Pulido and Sukrit Singh and Paula Linh Kramer and Guilermo Pérez-Hernández and Andrea Volkamer and John D Chodera},
 doi = {10.33011/livecoms.6.1.3875},
 editor = {David L. Mobley and Michael R. Shirts and Daniel M. Zuckerman},
 journal = {Living Journal of Computational Molecular Science (LiveCoMS)},
 month = {11},
 number = {1},
 pages = {3875-3875},
 publisher = {University of Colorado Boulder, Boulder, Colorado, USA},
 title = {The Journey of Data: Lessons Learned in Modeling Kinase Affinity, Selectivity, and Resistance [Article v1. 0]},
 url = {https://www.dfki.de/fileadmin/user_upload/import/16917_The_Journey_of_Data_Lessons_Learned_in_Modeling_Ki.pdf},
 volume = {6},
 year = {2025}
}
DOI

2024

1 paper

Structural activity prediction models recover known binding modes (Poster abstract)

2024 — ICML'24 Workshop ML for Life and Material Science: From Theory to Industry Applications. International Conference on Machine Learning (ICML-2024)

@inproceedings{backenkohlerStructuralActivityPredictionModels2024,
 author = {Michael Backenköhler and Joschka Groß and Paula Linh Kramer and Verena Wolf and Andrea Volkamer},
 booktitle = {ICML'24 Workshop ML for Life and Material Science: From Theory to Industry Applications. International Conference on Machine Learning (ICML-2024)},
 publisher = {Openreview},
 title = {Structural activity prediction models recover known binding modes (Poster abstract)},
 url = {https://openreview.net/forum?id=9O27GTKm1B},
 year = {2024}
}
URL

2023

1 paper

TeachOpenCADD goes Deep Learning: Open-source Teaching Platform Exploring Molecular DL Applications

2023 — ChemRxiv

PDF
@article{backenkohlerTeachOpenCADDGoesDeepLearning2023,
 author = {Michael Backenköhler and Paula Linh Kramer and Joschka Groß and Gerrit Großmann and Roman Joeres and Azat Tagirdzhanov and Dominique Sydow and Hamza Ibrahim and Floriane Odje and Verena Wolf and Andrea Volkamer},
 doi = {10.26434/chemrxiv-2023-kz1pb},
 journal = {ChemRxiv},
 month = {5},
 pages = {---},
 publisher = {ChemRxiv.org},
 title = {TeachOpenCADD goes Deep Learning: Open-source Teaching Platform Exploring Molecular DL Applications},
 url = {https://doi.org/10.26434/chemrxiv-2023-kz1pb},
 volume = {ChemRxiv Online},
 year = {2023}
}
DOI URL

2022

1 paper

A comprehensive approach for genome-wide efficiency profiling of DNA modifying enzymes

2022 — Cell Reports Methods

@article{kyriakopoulosAComprehensiveApproachFor2022,
 author = {Charalampos Kyriakopoulos and Karl Nordström and Paula Linh Kramer and Judith Yumiko Gottfreund and Abdulrahman Salhab and Julia Arand and Fabian Müller and Ferdinand von Meyenn and Gabriella Ficz and Wolf Reik and Verena Wolf and Jörn Walter and Pascal Giehr},
 doi = {10.1016/j.crmeth.2022.100187},
 journal = {Cell Reports Methods},
 month = {3},
 number = {3},
 pages = {1-23},
 publisher = {Cell Press},
 title = {A comprehensive approach for genome-wide efficiency profiling of DNA modifying enzymes},
 volume = {2},
 year = {2022}
}
DOI

Teaching (1)