Closing the data loop in AI-driven drug discovery

AI is accelerating drug discovery by enabling predictive design of molecular candidates and reducing the need for physical screening, though all AI-generated candidates still require lab validation. The application highlights critical challenges around data quality, including the need for negative results data and improved data integrity verification, as well as the requirement for integrated lab infrastructure to enable autonomous labs-in-the-loop.

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