Kinases

Evaluation beyond goodness of fit: Quantifying biophysical alignment of AI models for kinase-centric drug discovery
Accurately modeling interactions between small molecules and proteins using machine learning (ML) remains a central challenge in modern …
@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
The Journey of Data: Lessons Learned in Modeling Kinase Affinity, Selectivity, and Resistance [Article v1. 0]
Recent advances in machine learning (ML) are reshaping drug discovery. Structure-based ML methods use physically-inspired models to …
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@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
Guided docking as a data generation approach facilitates structure-based machine learning on kinases
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D …
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@article{backenkohlerGuidedDockingAsA2024,
 author = {Michael Backenköhler and Joschka Groß and Verena Wolf and Andrea Volkamer},
 doi = {10.1021/acs.jcim.4c00055},
 journal = {Journal of Chemical Information and Modeling (JCIM)},
 month = {5},
 number = {10},
 pages = {4009-4020},
 publisher = {ACS Publications, Washington DC},
 title = {Guided docking as a data generation approach facilitates structure-based machine learning on kinases},
 url = {https://pubs.acs.org/doi/10.1021/acs.jcim.4c00055},
 volume = {64},
 year = {2024}
}
DOI URL