Fig. 2

Conceptualization of Error and the error bound. We define Error as the absolute difference between \({\mathrm{\Delta \Delta }G}_{FoldX}\) and \({\mathrm{\Delta \Delta }G}_{exp}\) (Eq. 1). We construct a linear regression model that predicts the Error, and the model’s 95% prediction interval (the upper bound of the interval, specifically, since the prediction is an unsigned magnitude) captures \({\mathrm{\Delta \Delta }G}_{exp}\) (the “ground truth”) approximately 95% of the time