# Application of SSIR Method for the Design of Fungicides
## 4 Materials and Methods  
SSIR is variable selector [1; 3] and finds a wide field of application in QSAR studies when dealing with families of congeneric compounds. Here, a set of congeneric fungicides [4] is analyzed. This molecular family has been chosen in order to demonstrate how SSIR produces the results (ranks molecules) that point to the same direction as the ones of a former work. Additionally, the congeneric pool of molecules in reference [4] can be easily codified as SSIR demands. The details of SSIR method will not be repeated here in full, as they can be found elsewhere [2; 16; 17]. The method deals with the different substitution patterns of a congeneric family (i.e., having a common scaffold) by combining the substituents or residues that can be accommodated at each place. Each place of substitution is called a site, and each substituent assignable to each site defines a level. The SSIR algorithm consists of combining one or more sites and, for each combination of them, generating permutations of all the involved levels. Each combination of sites and respective residues generates a rule. Once a rule is generated, the training database is checked and, if several molecules satisfy the rule in this molecular subset, the balance between the number compounds of interest (active) and of no-interest (non-active) is evaluated. For instance, one rule found in the training calculation for CDM is the following:  
* |C***|A  
This means that the rule does not care about the substitutions at sites number 1 and 3-5. The rule is of order 2 and, consequently, only two sites are considered. This rule can be read as \"No substituent C at site 2 and, simultaneously, no substituent A at site 6\". This rule also shows that both choices, affirmations of presence of a residue or negations of presence (i.e., absence) can be part of the rule. The list of 34 training structures of Table 1 (CDM column) shows 8 molecules that fit the rule, and in this subset of 8, 6 analogues are active. A simple probabilistic calculation based on the hypergeometric probability reports the degree of significance (\(p\)-value) associated with the rule obtained. Rules with a \(p\)-value equal or less that a pre-specified one are called significant rules. For our example for rule (1), the associated \(p\)-value is obtained by adding the probabilities that the rule has of collecting 6 structures of interest (actual case) or more. This corresponds to the sum of the probabilities of collecting 6, 7 or 8 active compounds, which is obtained by adding the following products and quotients of combinatorial numbers [17]:  
C(9,6)-C(25,2)/C(34,8) + C(9,7)-C(25,1)/C(34,8) + C(9,8)-C(25,0)/C(34,8) = 0.0014. Since the \(p\)-value threshold chosen a priori was 0.01, this rule is declared significant in the present model. All the significant rules found are compiled, conforming an expert system able of evaluating any compound belonging to the same congeneric family. Therefore, any test or external molecule can be confronted with the set of significant rules. Each significant rule that fits or matches the compound gives it a positive or negative vote, as explained in references [2; 16; 17]. Continuing with our example, an external compound such as BBAFBD will receive a vote from rule (1), as it matches. At the end of the count, each evaluated compound has a final number of positive or negative votes. The sum of votes ranks the evaluated molecules. Candidates with more net positive votes are expected to be more likely to be of interest (active in our case). In other words, the molecular categorization (which may be unknown along the evaluated elements) is expected to correlate with the ranking. In particular, if a series of molecules with known properties are being ranked (e.g., the training set or a test or evaluation set), the AU-ROC value is a good parameter able of evaluating the method performance (see below) [18].