Author

E. Smorodina

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein structure· 2 Research Focus:protein structure prediction· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Approach:statistical model· 1

Frequent Co-Authors

V. Greiff· 2 shared A. H. Laustsen· 1 shared A. Olar· 1 shared A. Prosz· 1 shared A. Slabodkin· 1 shared A. Ward· 1 shared A.-L. M. Fischer· 1 shared B. B. Mehta· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBiophysics

V. Greiff

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Author profile →

2 papers

Recent·Most cited

Assessing AF2's ability to predict structural ensembles of proteins

J. R. Riccabona, F. C. Spoendlin, A.-L. M. Fischer, J. R. Loeffler, P. K. Quoika, T. P. Jenkins, J. A. Ferguson, E. Smorodina, A. H. Laustsen, V. Greiff, S. Forli, A. Ward, C. Deane, M. L. Fernandez-Quintero

This study evaluates AF2's ability to predict protein structural ensembles by comparing its results with MD simulations and NMR data for four test proteins.

bioRxiv · Apr 16, 2024Biophysics

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

P. A. Robert, R. Akbar, R. Frank, M. Pavlovic, M. Widrich, I. Snapkov, A. Slabodkin, M. Chernigovskaya, L. Scheffer, E. Smorodina, P. Rawat, B. B. Mehta, M. H. Vu, I. Froberg Mathisen, A. Prosz, , V. Greiff

The paper introduces Absolut!, a software suite generating synthetic antibody-antigen structures to benchmark machine learning methods for antibody specificity prediction.

bioRxiv · Jul 6, 2021Immunology