Democratizing Drug Discovery with an Orchestrated, Knowledge-Driven Multi-Agent Team for User-Guided Therapeutic Design
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915694155661312 |
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| author | Suzuki, Takahide Nakanishi, Kazuki Fujiwara, Takashi Shimizu, Hideyuki |
| author_facet | Suzuki, Takahide Nakanishi, Kazuki Fujiwara, Takashi Shimizu, Hideyuki |
| contents | Therapeutic discovery remains a formidable challenge, impeded by the fragmentation of specialized domains and the execution gap between computational design and physiological validation. Although generative AI offers promise, current models often function as passive assistants rather than as autonomous executors. Here, we introduce OrchestRA, a human-in-the-loop multi-agent platform that unifies biology, chemistry, and pharmacology into an autonomous discovery engine. Unlike static code generators, our agents actively execute simulations and reason the results to drive iterative optimization. Governed by an Orchestrator, a Biologist Agent leverages deep reasoning over a massive knowledge graph (>10 million associations) to pinpoint high-confidence targets; a Chemist Agent autonomously detects structural pockets for de novo design or drug repositioning; and a Pharmacologist Agent evaluates candidates via rigorous physiologically based pharmacokinetic (PBPK) simulations. This architecture establishes a dynamic feedback loop where pharmacokinetic and toxicity profiles directly trigger structural reoptimization. By seamlessly integrating autonomous execution with human guidance, OrchestRA democratizes therapeutic design, transforming drug discovery from a stochastic search to a programmable evidence-based engineering discipline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Democratizing Drug Discovery with an Orchestrated, Knowledge-Driven Multi-Agent Team for User-Guided Therapeutic Design Suzuki, Takahide Nakanishi, Kazuki Fujiwara, Takashi Shimizu, Hideyuki Artificial Intelligence Multiagent Systems Quantitative Methods Therapeutic discovery remains a formidable challenge, impeded by the fragmentation of specialized domains and the execution gap between computational design and physiological validation. Although generative AI offers promise, current models often function as passive assistants rather than as autonomous executors. Here, we introduce OrchestRA, a human-in-the-loop multi-agent platform that unifies biology, chemistry, and pharmacology into an autonomous discovery engine. Unlike static code generators, our agents actively execute simulations and reason the results to drive iterative optimization. Governed by an Orchestrator, a Biologist Agent leverages deep reasoning over a massive knowledge graph (>10 million associations) to pinpoint high-confidence targets; a Chemist Agent autonomously detects structural pockets for de novo design or drug repositioning; and a Pharmacologist Agent evaluates candidates via rigorous physiologically based pharmacokinetic (PBPK) simulations. This architecture establishes a dynamic feedback loop where pharmacokinetic and toxicity profiles directly trigger structural reoptimization. By seamlessly integrating autonomous execution with human guidance, OrchestRA democratizes therapeutic design, transforming drug discovery from a stochastic search to a programmable evidence-based engineering discipline. |
| title | Democratizing Drug Discovery with an Orchestrated, Knowledge-Driven Multi-Agent Team for User-Guided Therapeutic Design |
| topic | Artificial Intelligence Multiagent Systems Quantitative Methods |
| url | https://arxiv.org/abs/2512.21623 |