Artificial intelligence (AI) is rapidly transforming drug discovery, with models now capable of generating hypotheses and running experiments autonomously. This acceleration is significant, yet it highlights a critical concern: the United States is lagging behind China in building biological infrastructure. If the U.S. does not enhance its capabilities, it risks losing its competitive edge in the future of medicine.
Current AI systems primarily learn from existing scientific literature, akin to training pilots solely in simulators. This approach lacks the real-world complexities necessary for true adaptability. The success of AI in other domains, such as Amazon Web Services, underscores the importance of robust infrastructure that allows for large-scale experimentation and continuous improvement. Without a similar foundation in biology, drug development remains largely speculative.
Biological data centers, equipped with robotic systems that maintain standardized human tissues, represent a pivotal shift. These facilities enable the testing of drug candidates in environments that closely mimic human biology, allowing for earlier detection of toxicities and more reliable outcomes. As regulatory frameworks evolve to embrace human-relevant evidence, the industry faces increasing pressure to prioritize human data over animal models. The convergence of ethical considerations and economic realities suggests that the future of drug discovery will be defined by the infrastructure we build today. If the U.S. aims to lead in this new era, immediate action is essential.
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