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One of the challenges with predictive modeling is how to quantify the reliability of the models' predictions on new objects. In this work we give an introduction to conformal prediction, a framework that sits on top of traditional machine learning algorithms and which outputs valid confidence estimates to predictions from QSAR models in the form of prediction intervals that are specific to each predicted object. For regression, a prediction interval consists of an upper and a lower bound. Fordoi:10.1016/j.xphs.2020.09.055 pmid:33075380 fatcat:o5gigc6plffm7n7nxxvnsirwri