プロフィール詳細

プロジェクトを作成

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Anastasia S.に依頼

Armenia

Structural Biologist | Expert in Molecular Modeling & Drug Discovery

プロフィール概要

I am a Structural Biologist committed to utilizing advanced computational techniques to advance drug discovery. My areas of expertise encompass molecular modeling, molecular docking, and molecular dynamics simulations, which allow for a comprehensive exploration of macromolecular interactions. I focus on pharmacophore modeling and computer-aided drug discovery (CADD). I employ various tools, including AutoDock, ICM-Pro, PLANTS, SPORES, and the Schrödinger Suite, in addition to GROMACS and AMBER, for conducting in-depth molecular simulations. I utilize PyMOL and Chimera for visualization and analysis, applying rigorous statistical methodologies.

専門分野

• ★ Molecular Modeling
• ★ Computational Biology
• ★ Drug Design
• ☆ Molecular Docking
• ☆ Computational Chemistry
• ☆ Microbiology
• ☆ Virology
• ☆ Coronaviruses
• ☆ Zika Virus
• ☆ Retrovirology
• ☆ Adenoviruses
• ☆ Molecular Virology
• ☆ Mathematical Biology
• ☆ Biochemistry
• ☆ Structural Biology
• ☆ Molecular Biology
• ☆ Omics
• ☆ Proteomics
• ☆ Data Analysis
• ☆ Computer Simulation/Computational Modeling
• ☆ Data Collection
• ☆ Artificial Intelligence
• ☆ Bioinformatics
• ☆ Systems Biology
• ☆ Structural Bioinformatics
• ☆ Knowledge Management
• ☆ Taxonomy/Classification
• ☆ Information Retrieval
• ☆ Library & Information Science
• ☆ Digital Libraries
• ☆ Information & Knowledge Management
• ☆ Data Storage
• ☆ Medical Libraries
• ☆ Drug Discovery
• ☆ Medicinal Chemistry
• ☆ Medical Writing
• ☆ Medical Content Writing
• ☆ SEO Medical Writing
• ☆ Continuing Medical Education (CME) Materials
• ☆ Research Summaries & Abstracts
• ☆ Medical Copywriting
• ☆ Technical & Scientific Writing
• ☆ Experimental Protocols
• ☆ Science Communication

サービス

• Writing
• Research
• Data & AI

Writing Medical Writing, Copywriting

Research Scientific and Technical Research, Systematic Literature Review, Secondary Data Collection

Data & AI Statistical Analysis, Data Visualization, Big Data Analytics, Data Cleaning, Data Processing

職務経験

Junior Specialist

International Institute of Molecular and Cell Biology

9月 2025 - 現在

Bioinformatician

Denovo Sciences

7月 2022 - 8月 2025

Senior Laboratory Assistant/Bioinformatician

Institute of Molecular Biology

4月 2021 - 8月 2025

学歴

Specialty (Medical Biochemistry and Biotechnology)

Russian-Armenian University

2019 - 2025

M1 (Innovative Drugs)

Université Bourgogne Franche-Comté

2023 - 2023

認定資格

• AI4YOUTH YouthPass

  HawkStars NGO, Pinhel, Guarda, Portugal

  10月 2024 - 現在

出版物

JOURNAL ARTICLE

Multi-target computational pipeline for discovery of pan-influenza neuraminidase inhibitors @ARTICLE{10.3389/fphar.2026.1721276,AUTHOR={Gevorgyan, Smbat and Ayvazyan, Marusya and Kharatyan, Levon and Shavina, Anastasiya and Abelyan, Narek and Khachatryan, Hamlet and Zakaryan, Hovakim },TITLE={Multi-target computational pipeline for discovery of pan-influenza neuraminidase inhibitors},JOURNAL={Frontiers in Pharmacology},VOLUME={Volume 17 - 2026},YEAR={2026},URL={https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1721276},DOI={10.3389/fphar.2026.1721276},ISSN={1663-9812},ABSTRACT={The continuous evolution of influenza A and B viruses, coupled with the emergence of drug resistance, creates a pressing need for novel antiviral agents with broad-spectrum activity. The viral neuraminidase enzyme remains a prime target, but its structural variability across different strains complicates the discovery of universal inhibitors. To address this challenge, we developed and implemented a multi-target computational pipeline designed to identify pan-influenza neuraminidase inhibitors. Our strategy involved high-precision molecular docking of a curated library containing 499,721 compounds against three structurally distinct neuraminidase representatives from influenza A (H1N1, H2N2) and influenza B viruses. Hits were prioritized using a cascade of energetic and geometric filters, followed by a rigorous two-tiered validation using extensive molecular dynamics simulations. This validation not only confirmed binding stability on the primary target but also critically assessed whether candidates maintained stable interactions across the other neuraminidase subtypes. This cross-validation approach was essential for eliminating subtype-specific binders, ultimately identifying ten compounds with robust, pan-influenza binding profiles. Notably, the successful identification of a diastereomer of the established drug zanamivir among the top candidates provides strong validation for the pipeline’s ability to find biologically relevant scaffolds. Overall, this work demonstrates the integration of multi-target screening with cross-validated molecular dynamics (cross-MD) that overcame target variability and yielded ten promising hits candidates for next-generation anti-influenza therapeutics.}} . Frontiers in Pharmacology.

Sordyl, Dominik, Boileau, Etienne, Bernat, Agata, Maiti, Satyabrata, Mukherjee, Sunandan, Moafinejad, S Naeim, Farsani, Masoud Amiri, Shavina, Anastasiya, Cappannini, Andrea, Agostini, Giada, et al.(2025). MODOMICS: a database of RNA modifications and related information. 2025 update and 20th anniversary . Nucleic Acids Research. 54. (D1). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString D219-D225.

Levon Kharatyan, Smbat Gevorgyan, Hamlet Khachatryan, Anastasiya Shavina, Astghik Hakobyan, Mher Matevosyan, Hovakim Zakaryan (2025). Data-driven discovery of chemical signatures for developing new inhibitors against human influenza viruses . BMC Chemistry.

Smbat Gevorgyan, Hamlet Khachatryan, Anastasiya Shavina, Sajjad Gharaghani, Hovakim Zakaryan (2024). Targeting SARS-CoV-2 main protease: a comprehensive approach using advanced virtual screening, molecular dynamics, and in vitro validation . Virology Journal.

Hamlet Khachatryan, Mher Matevosyan, Vardan Harutyunyan, Smbat Gevorgyan, Anastasiya Shavina, Irina Tirosyan, Yeva Gabrielyan, Marusya Ayvazyan, Marine Bozdaganyan, Zeynab Fakhar, et al. (2024). Computational evaluation and benchmark study of 342 crystallographic holo-structures of SARS-CoV-2 Mpro enzyme . Scientific Reports.

Chilingaryan, Garri, et al. "Advanced virtual screening enables the discovery of a host-targeting and broad-spectrum antiviral agent." Antiviral Research 217 (2023): 105681 .

Roza Izmailyan, Mher Matevosyan, Hamlet Khachatryan, Anastasiya Shavina, Smbat Gevorgyan, Artur Ghazaryan, Irina Tirosyan, Yeva Gabrielyan, Marusya Ayvazyan, Boris Martirosyan, et al. (2024). Discovery of new antiviral agents through artificial intelligence: In vitro and in vivo results . Antiviral Research.

Garri Chilingaryan, Roza Izmailyan, Rafayela Grigoryan, Anastasiya Shavina, Erik Arabyan, Hamlet Khachatryan, Narek Abelyan, Mher Matevosyan, Vardan Harutyunyan, Gayane Manukyan, et al. (2023). Advanced virtual screening enables the discovery of a host-targeting and broad-spectrum antiviral agent . Antiviral Research.