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Predictive modeling in drug development can now provide actionable confidence measures, transforming how we assess molecular properties under shifting conditions.
In-training adaptation not only maintains valid coverage but also acts as a debiasing operator, leading to narrower predictive intervals without sacrificing accuracy.
