AI in drug development is entering a more demanding phase.
As candidates move through the development pipeline, they face increasingly complex challenges across biological validation, translational risk, patient heterogeneity, and the timing of evidence.
Many candidates enter the pipeline. Far fewer progress toward clinical validation.
The next frontier is not simply increasing the number of candidates.
It is generating robust, validated, decision ready evidence earlier in the development process, enabling more informed decisions at critical stages.
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