Automatic Perception of Chemical Similarities Between Metabolic Pathways

Metabolic pathways are at the crossroad between the chemical world of small molecules and the biological world of enzymes, genes and regulation. Methods for their processing are therefore required for a great variety of applications. The work presented here reports a new method to encode metabolic pathways and reactomes of organisms based on the MOLMAP approach. Pathways are represented from features of the metabolites involved in their reactions enabling to automatically perceive chemical similarities, and making no use of EC numbers. MOLMAP descriptors are based on atomic topological and physicochemical features of the bonds involved in reactions. The results show that self-organizing maps (SOM) can be trained with MOLMAPs of pathways to automatically recognize similarities between pathways of the same type of metabolism. The study also illustrates the possibility of applying the MOLMAP methodology at progressively higher levels of complexity, bridging chemical and biological information, and going all the way from atomic properties to the classification of organisms.