Author

Tigran Aghajanyan

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· 2

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Garegin A. PapoianiD· 2 shared Garik Petrosyan· 2 shared Khachik SmbatyaniD· 2 shared Tsolak GhukasyaniD· 2 shared Vahagn AltunyaniD· 2 shared Aram Bughdaryan· 1 shared Aram DavtyaniD· 1 shared Arman Fahradyan· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersBiophysics

Garegin A. PapoianORCID

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2 papers

Recent·Most cited

Overcoming the accuracy-generalization tradeoff in docking and scoring for prospective virtual screening

Garik Petrosyan, Vahagn Altunyan, Tsolak Ghukasyan, Tigran M. Abramyan, Grigor Arakelov, Aram Davtyan, Tigran Aghajanyan, Iskander Nakipov, Gagik Navasardyan, Arman Fahradyan, Hazarapet Tunanyan, Arman Simonyan, Paweł Janczyk, DAS Silva, Hayk Saribekyan, , Garegin A. Papoian

This paper introduces DODock and DOScore, hybrid ML/physics frameworks for docking and scoring that improve accuracy while generalizing better to novel molecules, showing significant improvements in hit rates for virtual screening.

bioRxiv · Aug 3, 2026 · PreprintBiophysics

Smart Distributed Data Factory: Volunteer Computing Platform for Active Learning-Driven Molecular Data Acquisition

Tsolak Ghukasyan, Vahagn Altunyan, Aram Bughdaryan, Tigran Aghajanyan, Khachik Smbatyan, Garegin A. Papoian, Garik Petrosyan

The paper introduces SDDF, a platform using volunteer computing and active learning to efficiently generate molecular conformation datasets for drug discovery.

Scientific Reports · Feb 27, 2025Peer-reviewedBiophysics