Integrating conformal prediction into an RL-guided generative framework for cyclic peptide design improves optimization reliability by accounting for predictive uncertainty in permeability.
Both" predictionsclass=0 Number of samples with true label 0 (Equation 4) πππππππ‘π¦ππππ π =1 = Correct single labelππππ π =1 + Class
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Confidence is the key: how conformal prediction enhances the generative design of permeable peptides
Integrating conformal prediction into an RL-guided generative framework for cyclic peptide design improves optimization reliability by accounting for predictive uncertainty in permeability.