Author

Daria B. Kokh

Heidelberg Institute for Theoretical StudiesORCID 0000-0002-4687-6572Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Biophysics· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Approach:statistical model· 3 Assay:protein structure· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Rebecca C. WadeiD· 3 shared Giulia D’ArrigoiD· 2 shared Ariane Nunes‐AlvesiD· 1 shared Jui-Hung YuaniD· 1 shared Mislav Brajković· 1 shared Nico Herb· 1 shared Stefan RichteriD· 1 shared Sungho Bosco HaniD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 14 mutual co-author links

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3 shared papersBioinformatics

Rebecca C. WadeORCID

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3 papers

Recent·Most cited

On the determinants of residence times and dissociation mechanisms of complexes of interleukin-13 with its low and high affinity receptors

Nico Herb, Mislav Brajković, Giulia D’Arrigo, Daria B. Kokh, Rebecca C. Wade

This study uses molecular dynamics simulations to analyze how IL-13 binds and dissociates from its receptors, identifying key determinants of residence times and dissociation mechanisms.

bioRxiv · Aug 21, 2026 · PreprintBiophysics

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by {tau}RAMD

Giulia D’Arrigo, Daria B. Kokh, Ariane Nunes‐Alves, Rebecca C. Wade

This paper introduces {tau}RAMD, a method to compute protein-protein dissociation rates and mechanisms, showing good agreement with experimental data and identifying mutation-sensitive hotspots.

Communications Biology · Sep 17, 2024Peer-reviewedBioinformatics

Druggability Assessment in TRAPP using Machine Learning Approaches

Jui-Hung Yuan, Sungho Bosco Han, Stefan Richter, Rebecca C. Wade, Daria B. Kokh

The paper develops two models, TRAPP-LR and TRAPP-CNN, to predict protein druggability and its variation with conformational changes, integrated into the TRAPP tool for analyzing binding pocket dynamics.

Journal of Chemical Information and Modeling · Feb 27, 2020Peer-reviewedBioinformatics