A standard metric in the machine learning community to classify the performance of prediction algorithms is the area under the receiver operating characteristic (ROC) curve (AUC). It can be interpreted as the probability that a randomly chosen missing reaction (a true positive) is assigned a higher score than a randomly chosen pair of unconnected vertices (a true negative) . Upon successful model selection (see Fig. , S2, and supplementary text) all AUC values after the year 1890 take values around 0.9, indicating excellent performance of the algorithm, Figure . Thus, the probability that a randomly chosen positive will rank higher than a randomly chosen negative is 90%.