# Application of SSIR Method for the Design of Fungicides
## 2 Results  
### Results for CDM  
The training set included 34 structures with a defined dichotomous CDM value (see Table 1), 9 were active and 25 non-active. In this case SSIR generated 520 rules of order two. Among them, 34 rules were statistically significant when the threshold \(p\)-value was set to 0.010. The minimum \(p\)-value found for a rule was 0.0002. Applying the rules to the same training set produced a classifier with an Area Under the Receiver Operator Characteristic curve (AU-ROC) of 0.953. This value was qualitatively maintained for the same set of rules in a leave-one-out (LOO) test (AU-ROC = 0.909). Figure 1 shows the ROC curve plot for this cross-validation calculation. The red dot indicates the optimal classification point. It corresponds to the cutoff value of \(-9\) votes where 14 structures were classified as active, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & **SSIR** & **Of Interest** & **Of Interest** & **Of Interest** \\  & **Encoding** & **for CDM1** & **for RB2** & **for CGM3** \\ \hline P16 & AeABAA & N & (y) & N \\ P17 & ADABAA & N & N & N \\ P18 & ABAhaAA & N & Y & N \\ P19 & ABAFAA & (n) & Y & N \\ P20 & ABAgAA & N & Y & N \\ P21 & ACACAA & N & (y) & N \\ P22 & ADAhaA & Y & Y & N \\ P23 & ADAEAA & (n) & Y & N \\ P24 & ADAFAA & Y & Y & N \\ P25 & ABBBAA & N & N & N \\ P26 & ABABBA & N & N & N \\ P27 & AABBBA & (N) & (n) & N \\ P28 & ABABAB & Y & Y & N \\ P29 & ADABBA & N & Y & Y \\ P30 & ABAFBA & Y & Y & Y \\ P31 & ADAFAD & Y & N & N \\ P32 & ADAgAD & Y & Y & Y \\ P33 & ABAgAB & Y & Y & Y \\ P34 & ABABBD & (Y) & Y & Y \\ P35 & ABAFBD & Y & Y & Y \\ P36 & ADBgAD & Y & Y & Y \\ P37 & BAAFA & N & (N) & N \\ P38 & BBABAA & N & (y) & N \\ P39 & BBAgAA & N & (Y) & N \\ P40 & BDAFAD & N & N & N \\ \hline \hline \end{tabular} 1
[FOOTNOTE:1]Footnote 1: Values of interest have been defined here as those of at least 80% percent of susceptibility (for a dose of 100 mg/L). 2
[FOOTNOTE:2]Footnote 2: Values of interest: those with a percentage susceptibility of at least 100% (for a dose of 2.8 mg/L). 3  
\end{table} 
Table 1: _Cont._and 9 were actually of interest (true positive, TP) and 5 were not (false positive, FP). At this point, the figures of merit are listed in Table 2.  
The total number of residue combinations in the library is \(2\times 5\times 2\times 8\times 2\times 3=960\) different analogues. Since 34 have a known activity classification, SSIR produced predictions for the remaining 926 structures. Once the votes derived from significant rules are collected, there are nine new structures with a maximum of positive votes and which potentially may be of interest:  
ADAFAB, ADBFAD, ADACAD,  
ADBFAB, ADADAD, ADAgAB,  
ADAhAD, ADAiAD, ADAFBD  
The model also provided predictions for the cases where the experimental value is not known in Table 1 (see the values in parentheses). In it, a capital letter means strong evidence (maximum number of positive or negative votes in the ranking), and a lower case letter signifies only some evidence (intermediate number of positive or negative votes in the ranking). In particular, from the training model, a prediction can be obtained for the six molecules with unknown value. Five of those molecules received a cumulated number of negative votes. This means that SSIR predicts that they will probably not be active. Only  
Figure 1: AU-ROC plot for CDM obtained with a LOO cross-validation calculation.