Graph-based processing of macromolecular information

The complex information encoded into the element connectivity of a system gives rise to the possibility of graphical processing of divisible systems by using the Graph theory. An application in this sense is the quantitative characterization of molecule topologies of drugs, proteins and nucleic acids, in order to build mathematical models as Quantitative Structure - Activity Relationships between the molecules and a specific biological activity. These types of models can predict new drugs, molecular targets and molecular properties of new molecular structures with an important impact on the Drug Discovery, Medicinal Chemistry, Molecular Diagnosis, and Treatment. The current review is focused on the mathematical methods to encode the connectivity information in three types of graphs such as star graphs, spiral graphs and contact networks and three in-house scientific applications dedicated to the calculation of molecular graph topological indices such as S2SNet, CULSPIN and MInD-Prot. In addition, some examples are presented, such as results of this methodology on drugs, proteins and nucleic acids, including the Web implementation of the best molecular prediction models based on graphs.This work is supported by "Collaborative Project on Medical Informatics (CIMED)" PI13/00280 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 20l3- 2016 and the European Regional Development Funds (FEDER) and by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. GRC2014/049), the Galician Network for Colorectal Cancer Research (REGICC) (Ref. R2014/039) and by the Galician Network of Drugs R+D REGID (R2014/025) and the European Fund for Regional Development (FEDER) in the European Union