Author

Joschka Groß

German Research Centre for Artificial IntelligenceORCID 0000-0002-8957-8807Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Alexander Matthew PayneiD· 1 shared Andrea VolkameriD· 1 shared Benjamin KaminowiD· 1 shared Corey TayloriD· 1 shared David SchalleriD· 1 shared Guillermo Pérez‐HernándeziD· 1 shared Iván PulidoiD· 1 shared Jaime Rodrı́guez-GuerraiD· 1 shared

Activity by Year

2025-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBiophysics

Alexander Matthew PayneORCID

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1 paper

Recent·Most cited

Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance

Raquel López-Ríos de Castro, Jaime Rodrı́guez-Guerra, David Schaller, Talia B. Kimber, Corey Taylor, Jessica B. White, Michael Backenköhler, Joschka Groß, Alexander Matthew Payne, Benjamin Kaminow, Iván Pulido, Sukrit Singh, Paula Linh Kramer, Guillermo Pérez‐Hernández, Andrea Volkamer, John D. Chodera

This paper describes the development of KinoML, a framework for structure-enabled machine learning in drug discovery, focusing on kinases, and highlights key lessons in data management and model reproducibility.

Living Journal of Computational Molecular Science · Nov 11, 2025Peer-reviewedBiophysics