Author

Anna Litvin

Skolkovo Institute of Science and TechnologyORCID 0009-0002-9169-8370Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:benchmark study· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:protein sequence· 1 Assay:protein structure· 1

Frequent Co-Authors

Daria FrolovaiD· 2 shared Dmitry N. IvankoviD· 2 shared Ivan Valer'evich OseledetsiD· 2 shared Marina A. PakiD· 2 shared Alexander V. ShapeeviD· 1 shared Daria A. RyabchenkoiD· 1 shared Ilya SharoviD· 1 shared Kamil GarifulliniD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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2 shared papersBioinformatics

Daria FrolovaORCID

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2 papers

Recent·Most cited

Bento: Benchmarking Classical and AI Docking on Drug Design-Relevant Data

Marina A. Pak, Daria Frolova, Sergei A. Nikolenko, Talgat Daulbaev, Daria A. Ryabchenko, Anna Litvin, Pavel E. Gurevich, Kamil Garifullin, Alexander V. Shapeev, Ivan Valer'evich Oseledets, Dmitry N. Ivankov

This study benchmarks 11 protein-ligand interaction prediction tools, showing classical and deep learning methods perform similarly on drug-like ligands, while co-folding tools excel on complex ligands and all struggle with generalization to unseen pockets.

bioRxiv · Dec 30, 2025 · PreprintBioinformatics

MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding

Daria Frolova, Marina A. Pak, Anna Litvin, Ilya Sharov, Dmitry N. Ivankov, Ivan Valer'evich Oseledets

The paper introduces MULAN, a multimodal protein language model that integrates sequence and structure information, showing improved performance on downstream tasks compared to sequence-only and structure-aware models.

Bioinformatics Advances · Dec 26, 2024Peer-reviewedBioinformatics