“…More recently, Minerali et al 58 have generated and compared ML algorithms for predicting DILI with in‐house software Assay Central (https://www.collaborationspharma.com/assay-central), using data previously collated by Pfizer and AstraZeneca research groups, along with data from the FDA, performing an ROC of 81%, sensitivity of 74%, specificity of 76%, and accuracy of 75% for the best Bayesian model based on the DILI‐concern category from the DILI Rank database. Noncommercial software has been developed by Mora et al, freely available at http://tomocomd.com/apps/ptoxra for the DILI prediction with ML models on a training set of 1075 molecules, attaining an accuracy of 84%, a sensibility of 89%, specificity of 76%, and ROC of 90%; 59 In the pharmaceutical company AstraZeneca Williams et al 60 predicted DILI by using ML, quantifying the probability that a compound falls into either low, medium, or high‐severity categories, with an accuracy of 63%. For a binary yes/no DILI prediction, the model obtained an accuracy of 86%, sensitivity of 87%, specificity of 85%, a positive predictive value of 92%, and a negative predictive value of 78%.…”