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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21390 |
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| _version_ | 1866915534644183040 |
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| author | Zhavoronkov, Alex Leung, Chuen Yan |
| author_facet | Zhavoronkov, Alex Leung, Chuen Yan |
| contents | The escalating cost, extended timelines, and low success rates in pharmaceutical research demand a fundamental rethinking of biotechnology R&D infrastructure. This chapter introduces the concept of the AI-Integrated Biotechnology Hub, a purpose-built research ecosystem uniting residential, commercial, clinical, and research facilities under a central, AI-driven operating system. Designed as a multi-sided platform, the hub leverages continuous, multi-modal health data collection, advanced smart living environments, and federated learning models to enable secure, privacy-preserving biomedical research. By integrating real estate, biotechnology facilities, research hospitals, and community services, the model maximizes data utility, accelerates drug discovery, and enhances resident well-being. Transparency, accountability, and ethical stewardship are critical pillars of governance, enacted through dynamic consent, data trusts, and multi-stakeholder oversight. Scalable across urban and vertical architectures, this paradigm offers a viable, sustainable pathway toward improving healthspan, fostering innovation, and reshaping the economics of global drug development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Engineering the Future of R&D: The Case for AI-Driven, Integrated Biotechnology Ecosystems Zhavoronkov, Alex Leung, Chuen Yan Physics and Society The escalating cost, extended timelines, and low success rates in pharmaceutical research demand a fundamental rethinking of biotechnology R&D infrastructure. This chapter introduces the concept of the AI-Integrated Biotechnology Hub, a purpose-built research ecosystem uniting residential, commercial, clinical, and research facilities under a central, AI-driven operating system. Designed as a multi-sided platform, the hub leverages continuous, multi-modal health data collection, advanced smart living environments, and federated learning models to enable secure, privacy-preserving biomedical research. By integrating real estate, biotechnology facilities, research hospitals, and community services, the model maximizes data utility, accelerates drug discovery, and enhances resident well-being. Transparency, accountability, and ethical stewardship are critical pillars of governance, enacted through dynamic consent, data trusts, and multi-stakeholder oversight. Scalable across urban and vertical architectures, this paradigm offers a viable, sustainable pathway toward improving healthspan, fostering innovation, and reshaping the economics of global drug development. |
| title | Engineering the Future of R&D: The Case for AI-Driven, Integrated Biotechnology Ecosystems |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2509.21390 |