# Application of SSIR Method for the Design of Fungicides
## 3 Discussion  
All the calculations performance has been evaluated by means of the AU-ROC or F\({}_{1}\) values. Regarding AU-ROC, usually, values above 0.75 are interpreted as acceptable or good, and this threshold was exceeded by the three properties studied. It also must be taken into account that the reported values here do not arise from a simple fit or training calculation, but from a particular kind of cross-validation, the LOO. This validation produces a prediction (i.e., reckoning of votes) for each molecule once the whole set of rules has been obtained with the remaining compounds. Hence, the excluded compound acts as a minimal validation or test set. As a consequence, the whole LOO procedure demands the construction of as many models as molecules in the set. The results obtained in this manner go beyond the quality of a single fit or training (i.e., a single calculation). On the other hand, F\({}_{1}\) scores (see Table 2) were above 70% for the individual models, despite the fact that only a value of \({}^{2}/_{3}\) was obtained for the merged one. It is worth mentioning that, by definition, F\({}_{1}\) did a good job balancing the precision values (PPV) here. This is because in all cases, sensitivities or recall (TPR) were 100%. This means that all active compounds were detected at the optimal classification points (red circles in the figures above). This is a good result, but is should also be noted that some non-active fungicides were classified as being actives (i.e., false positives) at those points. The accuracy takes the latter effect into account, and the F\({}_{1}\) score balances both fractions (see definition in materials and methods section). Again, it has to be mentioned that the F\({}_{1}\) values are evaluated among LOO cross-validated results. Among the three individual models, the RB one is the worst, maybe due to the lack of information that SSIR was able to extract from the data provided. The training model consisted of only three significant rules, and this number of rules does not change significantly along the successive LOO loops Admittedly, this pool of rules or micro-classifiers cannot operate as a good consensus expert, despite the \(p\)-value threshold is smaller than the one considered for the CDM model. In general, if a consensus system has to be constructed, it is better to have at a disposal a bigger number of rules despite their \(p\)-values are greater. This occurs for the CDM and CGM models (34 significant in a pool of 520, and 14 significant in a pool of 564, respectively for the single overall training calculation). The merged model performs well for the LOO predictions (AU-ROC of 91.1%), but it must be pointed out that it does not exhibit ideal conditions. Namely, the \(p\)-value threshold had to be put at a high value (0.50) for training in order to obtain an acceptable number of significant rules (279). Despite the minimal \(p\)-value found along these selected rules was low (0.0010), many of them exhibit non-standard values: 191 with \(p\)-values of 0.10 or greater and, among them, 98 with a \(p\)-value of 0.30 or greater). The model, constructed in this manner, acquired some stability and it seems that it is the reason why the LOO cross-validation produced the reported results. Some cancelation errors had probably played a role in the calculations. Again, this points to the concept that one must focus on a quite large amount of rules to act as an automated expert system instead to aim for a small number of very significant rules. In any case, the SSIR rules can be interpreted as simple combinations of features that tend to point to desired characteristics an active compound should have. Of course, a compound will exhibit a specific set of characteristics (i.e., the actual residues placed on the scaffold) that will not conform to all the rules. However, the pool of significant rules is expected to act as a good expert if it contains an adequate number of rules that, in a sense, covers all (or a majority of) interesting features.