**5. Selection of new drugs using machine learning techniques**

The *Leishmania* proteome is estimated to contain ~8,150 proteins based on the annotated genome of the sequenced species (Peacock., et al. 2007). However, fewer than 150 proteins have 3D structures in the PDB. This limits the use of docking-based strategies to search for anti-Leishmania compounds. An alternative strategy to associate active compounds with *Leishmania* targets is by using machine learning techniques. This approach is intended to find patterns on protein targets such as domains, post-translational modifications etc, that can be linked to a specific class of compounds. This system will "learn" these patterns and when challenged by proteins from the organism of interest it will predict the potential association for a particular compound. Two studies have applied this strategy to a particular set of protein targets (Bulashevska., et al. 2009; Thangudu., et al. 2010), employing the different techniques such as support vector machines (SVM) and Bayesian classifiers (BC). As a perspective, these methods could also be applied for drug search in *Leishmania;* however, the definition of protein patterns would be critical for establishing robust drug-target relationships.
