Chemical graph-based representations, molecular similarity

A brief introduction to the types of molecular representations typically encountered in MSA is presented at the beginning of Subheading 2. followed in Subheading 2.1. by a discussion of similarity measures based on chemical-graph representations. Although graph-based representations are the most familiar to chemists, their use has been somewhat limited in similarity studies due to the difficulty of evaluating the appropriate similarity measures. This section is followed by a discussion of similarity measures based on finite vector representations, the most ubiquitous types of representations. In these cases, the vector components can be of four types ... 

Chemical Graph-Based Repr entations In terms of general chemical applications, chanical graphs [71] are a natural representation to use for assessing molecular similarity. They are typically defined mathematically as an ordered triple of sets, Q = (V,E,L), where Q is the graph, V is the set of its t vertices ( atoms ),... 

The latter scenario is sometimes referred to as scaffold or lead hopping [22-25]. This is a formidable challenge for the descriptor and the similarity measure. While avoiding the chemical graph and atom type-based molecular representation, the essential features required for activity have to be retained. By definition, such a task will be prone to picking out false positives and, therefore, requires a fast search in large and diverse databases together with a tunable level of similarity. 

To exemplify a molecular similarity method, we employed here a 3D shape-based molecular similarity approach using OpenEye scientific software (OpenEye). A set of 27 molecules (Amoore, 1971) were compared to benzaldehyde (query molecule). The representation used here is based on the volume of each molecule. A conformational ensemble is built for the molecules in the database, whereas the conformation of the query remains fixed (the chemical nature of benzaldehyde does not entail different conformers, though in many cases the conformation of the query molecules might be complex and crucial). After the conformers of each molecule in the data set are built, each one of them is compared with the query and a similarity value is computed. For the particular program employed here (ROCS), the similarity is quantified as a score formed by two terms, one takes into account the chemical nature of the molecules while the other relies on molecular shape, such score is referred to as combo score. The maximum similarity value is 2 which can only be obtained from the comparison of a molecule with itself in the exact same conformation (perfect match). The normalized values (from 0 to 1) for the odor and combo score similarities are compared in the graph shown in Fig. 2.4. As can be observed, as the combo score increases, the odor similarity to benzaldehyde also increases. This correlation shows that part of the odor similarity was captured by the molecular... 

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