@inproceedings{solovev-etal-2025-madd,
title = "{MADD}: Multi-Agent Drug Discovery Orchestra",
author = "Solovev, Gleb Vitalevich and
Zhidkovskaya, Alina Borisovna and
Orlova, Anastasia and
Gubina, Nina and
Vepreva, Anastasia and
Golovinskii, Rodion and
Tonkii, Ilya and
Dubrovsky, Ivan and
Gurev, Ivan and
Gilemkhanov, Dmitry and
Chistiakov, Denis and
Aliev, Timur A. and
Poddiakov, Ivan and
Zubkova, Galina and
Skorb, Ekaterina V. and
Vinogradov, Vladimir and
Boukhanovsky, Alexander and
Nikitin, Nikolay and
Dmitrenko, Andrei and
Kalyuzhnaya, Anna and
Savchenko, Andrey",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.367/",
doi = "10.18653/v1/2025.findings-emnlp.367",
pages = "6956--6998",
ISBN = "979-8-89176-335-7",
abstract = "Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design."
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%0 Conference Proceedings
%T MADD: Multi-Agent Drug Discovery Orchestra
%A Solovev, Gleb Vitalevich
%A Zhidkovskaya, Alina Borisovna
%A Orlova, Anastasia
%A Gubina, Nina
%A Vepreva, Anastasia
%A Golovinskii, Rodion
%A Tonkii, Ilya
%A Dubrovsky, Ivan
%A Gurev, Ivan
%A Gilemkhanov, Dmitry
%A Chistiakov, Denis
%A Aliev, Timur A.
%A Poddiakov, Ivan
%A Zubkova, Galina
%A Skorb, Ekaterina V.
%A Vinogradov, Vladimir
%A Boukhanovsky, Alexander
%A Nikitin, Nikolay
%A Dmitrenko, Andrei
%A Kalyuzhnaya, Anna
%A Savchenko, Andrey
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F solovev-etal-2025-madd
%X Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.
%R 10.18653/v1/2025.findings-emnlp.367
%U https://aclanthology.org/2025.findings-emnlp.367/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.367
%P 6956-6998
Markdown (Informal)
[MADD: Multi-Agent Drug Discovery Orchestra](https://aclanthology.org/2025.findings-emnlp.367/) (Solovev et al., Findings 2025)
ACL
- 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, and Andrey Savchenko. 2025. MADD: Multi-Agent Drug Discovery Orchestra. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 6956–6998, Suzhou, China. Association for Computational Linguistics.