# Application of SSIR Method for the Design of Fungicides
## 1 Introduction  
The field of Quantitative Structure-Activity Relationship (QSAR) has evolved substantially in recent years, forming a robust paradigm. Nevertheless, sophisticated or field-adapted methods are being defined to refine prediction techniques. Typically, several procedures are combined to obtain the desired results. Considering the current scenario, it can be argued that simple methods should be defined and incorporated to obtain interpretable results or simply basic tools able of filtering the databases. This is the goal of the Superposing Significant Interaction Rules (SSIR). The SSIR procedure has proven to be a useful tool in several fields. For instance, in chromatography variable selection [1], when modeling Phospholipidosis by using binary fingerprints [2] or in the treatment of transcriptomics related to leukemia and prostate cancer [3], among others. This work constitutes a demonstration of SSIR performance by analyzing a group of 40 diphenylamine derivatives, i.e., a family of congeneric derivatives that share the same molecular skeleton or scaffold. The set has been taken from reference [4], which, in turn, collects data from references [5; 6; 7]. The main objective of this article is to show how reliable results can be obtained with SSIR in a very cheap and fast way. Expeditious methods are needed, as fungi cause considerable damage to agriculture annually. Furthermore, the emergence of resistance to known fungicides is an added problem to this situation. Therefore, the active search for new active agents to counteract these pests is needed [8]. For this search, the aid of computational methods, such as QSAR [9; 10], is increasingly being used in the development of new derivatives of known fungicides [11], in the design of new libraries for computational screening [12], in the identification of new lead structures [13] and in the screening of natural products [14]. Recently, [PERSON] et al. [4] published an interesting work that, using molecular topological indices treated with linear discriminant analysis, led to the classification of diphenylamine derivatives as fungicides against three species (cucumber downy mildew, rice blast and cucumber gray mold). The approach was based on the generation of molecules from a common scaffold. The authors evaluated more than 700 compounds and proposed some as new actives. [PERSON] and co-workers used their own software named DesMol2 [15]. This platform is not only able of generating a virtual library, but also generating ad hoc molecular descriptors. The calculated descriptors are topological. Hence, the program deals withsets of continuous variables. In addition, these variables can be included in a standard linear discriminant classifier. The classification served not only to generate a model from molecules with known fungicidal activities, but also to propose new structures having high activity potential. On the other hand, in our laboratory, we have developed the SSIR procedure, first described in ref. [16]. The SSIR method does not deal with continuous variables, but with symbolic descriptors. This feature greatly simplifies the process of coding common scaffold derivatives. In particular, only one symbol (e.g., a single letter) needs to be assigned to each residue that can be accommodated at a particular molecular scaffold site. The series of symbols can be repeated across different sites regardless of whether or not the residue is the same as those present at other sites. This is because placement in a different site changes the nature of the residue (unless symmetry issues arise). As a consequence, the potential molecular library becomes defined in SSIR framework simply making explicit the series of residues available at every substitution site. In practice, the figure and data reported in Table 2 of the paper by [PERSON] et al. are sufficient to potentially generate a subset of diphenylamine analogs. SSIR needs this information, along with knowledge of each analogue to be (enough) active or non-active as a fungicide.