The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
MADD: Multi-Agent Drug Discovery Orchestra
Open accessAbstract
Gleb Vitalevich Solovev, Alina Borisovna Zhidkovskaya, Anastasia Orlova, Nina Gubina, Anastasia Vepreva, Rodion Golovinskii, Ilya Tonkii, Ivan Dubrovsky, Ivan Gurev, Dmitry Gilemkhanov, Denis Chistiakov, Timur A. Aliev, Ivan Poddiakov, Galina Zubkova, Ekaterina V. Skorb, Vladimir Vinogradov, Alexander Boukhanovsky, Nikolay Nikitin, Andrei Dmitrenko, Anna Kalyuzhnaya, Andrey Savchenko. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.
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In the citing paper
“Supervisor-based multi-agent architectures can independently search, classify, and reconcile candidate mechanisms and compounds while maintaining explicit chains of provenance [27, 34, 48].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [48]
In the citing paper
“As recent implementations indicate, the value of agentic AI in drug discovery and personalized medicine emerges when reasoning, action, and evidence monitoring operate as an integrated system capable of accelerating insight without compromising reproducibility or governance [27, 33, 34, 45–48].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [48]
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Branda, F., Ahmed, M. M., Ciccozzi, M., Guzzi, P. H., & Scarpa, F. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag245 · Cited by 2
Closed-loop agentic AI in drug discovery
Chhetri, P. (2026) · RSC Medicinal Chemistry · doi:10.1039/d6md00319b · Cited by 2
(2344960), Y. G., Yu, L., & Li, H. (2026) · Journal of Superintelligence · doi:10.3390/superintelligence1020008
LGAgent: A Structured and Rule-Guided Multi-Agent Framework for Interpretable Legal ReasoningPreprint
Feng, X., Shu, F., Feng, M., Feng, Y., & Liu, H. F. (2026) · Research Square · doi:10.21203/rs.3.rs-9571992/v1
Bogdanova, N. G., Serebrova, S. Y., Климов, В. И., Esakova, E. Y., & Zhuravleva, M. V. (2026) · Meditsinskiy sovet = Medical Council · doi:10.21518/ms2026-418
Metadata
- Authors
- Solovev, G. V., Zhidkovskaya, A. B., Orlova, A., Gubina, N., Vepreva, A., Golovinskii, R., Tonkii, I., Dubrovsky, I., Gurev, I., Gilemkhanov, D., Chistiakov, D., Aliev, T. A., Poddiakov, I., Zubkova, G., Skorb, E. V., Vinogradov, V., Boukhanovsky, A., Nikitin, N., Dmitrenko, A., Kalyuzhnaya, A., & Savchenko, A.
- Affiliations
- ITMO University (RU)
- National Research University Higher School of Economics (RU)
- ORCID
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2025
- Published
- 2025
- Pages
- 6956-6998
- OpenAlex
- W4416034688
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 3
- FWCI
- 5.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Ministero dello Sviluppo Economico
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- multi-agent systems, drug discovery, drug repurposing, text mining, biomedical text mining
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- CoChat. (2026). Citation check: MADD: Multi-Agent Drug Discovery Orchestra. https://app.cochat.ai/papers/10.18653/v1/2025.findings-emnlp.367
- The paper
- Solovev, G. V., Zhidkovskaya, A. B., Orlova, A., Gubina, N., Vepreva, A., Golovinskii, R., Tonkii, I., Dubrovsky, I., Gurev, I., Gilemkhanov, D., Chistiakov, D., Aliev, T. A., Poddiakov, I., Zubkova, G., Skorb, E. V., Vinogradov, V., Boukhanovsky, A., Nikitin, N., Dmitrenko, A., . . . Savchenko, A. (2025). MADD: Multi-Agent Drug Discovery Orchestra. Findings of the Association for Computational Linguistics: EMNLP 2025. https://doi.org/10.18653/v1/2025.findings-emnlp.367
