## 2 Accuracy of Predictive Methods and What Is Required
### Cases Requiring only Qualitative Predictive Power  
For a catalyst, it can be important to know whether its selectivity is high, preferably close to 100%. This if different from, for example, the Tg case in the previous section, as now it only matters if a predicted high selectivity is indeed high in experiment. Whether low selectivity is predicted well or not is of no practical relevance. We illustrate this in the graphs in Figure 4. Although the model with R\({}^{2}\) = 0.999 shows an almost perfect correlation and the one with R\({}^{2}\) = 0.87 not, for the purpose of predicting high selectivities they do equally well--both predict the same 3 data points at the right-hand top of the graph as being good catalysts with high selectivity (both prediction and experiment show high selectivity). A similar argument applies to a completely different domain of application, the prediction of toxicity properties such as LD\({}_{50}\) type quantities (there are various such toxicity properties assessed by LD\({}_{50}\)). LD stands for \"Lethal Dose.\" LD\({}_{50}\) is the amount of a material that, given  
Figure 4: Experimental versus model selectivity for a realistic catalyst system. These plots serve to illustrate that in this case, where the main interest is to have a high selectivity, it does not matter that much if the method is not that good: in both cases, the same 3 data points represent the best catalysts (right hand top corner, star symbols). Figure 3: Comparison of predicted and measured transaminase catalysed conversions. For further details see the text and Reference [34]. death of 50% of a group of test animals. The LD\({}_{50}\) is one way to measure the short-term poisoning potential (acute toxicity) of a material. The lower this value the more toxic the substance is, as it means a low dose is already poisonous and deathly. The quantity is normally expressed it is -log LD\({}_{50}\) form and therefore the higher the value is the more toxic the compound. For the assessment of toxicity, this value is the threshold above which the compound is considered safe. Thus, as long as the predictions as well as the corresponding experimental data points are _both_ above this threshold, the predictions are useful and valid. Quite similar are other toxicological endpoints such as mutagenicity as evaluated by the AMES test (named after [PERSON]): it is qualified in a binary way too: mutagenic or not mutagenic. In the cases discussed in this section R\({}^{2}\) values or similar statistical quantities do not seem really meaningful here, as it will not be high for the data shown but the method gives reliable prediction. There are different parameters to assess the quality of these methods: sensitivity, specificity and concordance. These quantities are statistical measures of the performance of a binary classification test, for example a substance is toxic or non-toxic. _Concordance_ is the fraction of correct predictions, so if predicted safe it is indeed safe and in case predicted unsafe (toxic) indeed experimentally unsafe. Concordance levels below 70% are, however, no exception in the field, of toxicology. This implies that 30% of the predictions is wrong! Would not it be fair to ask one selves whether an experienced expert who also uses read-across can be equally good predictions? There is data around that support this, viz. Table 3. Although the predictive methods might have improved since this study, it is seen that human experts arrived at 75% good answers, which is higher (and this more reliable predictions) at the high end of values for predictive methods. Although it might seem very odd at first instance, it seems realistic to state that if it was to be known that for a certain toxicological endpoint only 20% of all compounds that have been experimentally screened is to be considered toxic, a default answer of 'non-toxic' for any new molecule would eventually (for a larger number of compounds) have a higher concordance than some of the current methods.