Author

Dmitry Vladimirovich Svetlichnyy

Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical and Biological AgencyORCID 0000-0002-3776-9871Unclaimed 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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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1 Assay:proteomics· 1

Frequent Co-Authors

Anna S. TashchilovaiD· 1 shared Anton Arturovich KeskinoviD· 1 shared Denis Y. LogunoviD· 1 shared Dmitry I. TychininiD· 1 shared Ivan KhokhloviD· 1 shared Konstantin Yu. KulebyakiniD· 1 shared L. N. ShcherbakovaiD· 1 shared Maksim A. Vigovskiy· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBioinformatics

Anna S. TashchilovaORCID

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2 papers

Recent·Most cited

scParadise: Tunable highly accurate multi-task cell type annotation and surface protein abundance prediction

Чечехина Елизавета Сергеевна, L. N. Shcherbakova, Maksim A. Vigovskiy, Тухватулин Амир Ильдарович, Denis Y. Logunov, Dmitry I. Tychinin, Dmitry Vladimirovich Svetlichnyy, Konstantin Yu. Kulebyakin, Vsevolod Tkachuk, Vadim I. Chechekhin

scParadise provides tools for accurate cell type annotation and surface protein prediction in scRNA-seq data, improving performance for rare cell types and clustering.

Nucleic Acids Research · Jun 4, 2026Peer-reviewedBioinformatics

DrugForm-DTA: Towards real-world drug-target binding Affinity Model

Ivan Khokhlov, Anna S. Tashchilova, Nikolai Bugaev-Makarovskiy, Olga Valentinovna Glushkova, Vladimir Sergeevich Yudin, Anton Arturovich Keskinov, Sergey Mikhailovich Yudin, Dmitry Vladimirovich Svetlichnyy, Veronika Igorevna Skvortsova

DrugForm-DTA predicts drug-target binding affinity using sequence-based inputs, achieving performance comparable to in-vitro experiments and outperforming molecular modeling methods.

Computational and Structural Biotechnology Journal · Jan 1, 2025Peer-reviewedBioinformatics