Author

Vahagn Altunyan

ORCID 0009-0002-0021-3789Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

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 Tigran Aghajanyan· 2 shared Tsolak GhukasyaniD· 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

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBiophysics

Garegin A. PapoianORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

BiophysicsbioRxivPreprintAug 3, 2026

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

Garik Petrosyan, Vahagn Altunyan, Tsolak Ghukasyan…·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.

DOI 10.64898/2026.08.03.742480

protein structuredeep learning

𝕏 PostBluesky

BiophysicsScientific ReportsPeer-reviewedFeb 27, 2025

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

Tsolak Ghukasyan, Vahagn Altunyan, Aram Bughdaryan…·Garik Petrosyan

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

1 citation

deep learningstatistical model

𝕏 PostBluesky