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arXiv · 2509.21390

Engineering the Future of R&D: The Case for AI-Driven, Integrated Biotechnology Ecosystems

Alex Zhavoronkov·Chuen Yan Leung
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Abstract

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.

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Alex Zhavoronkov, Chuen Yan Leung. 2025-09-24. Engineering the Future of R&D: The Case for AI-Driven, Integrated Biotechnology Ecosystems. https://arxiv.org/abs/2509.21390

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