title: Multi-target spectral moments for QSAR and Complex Networks study of antibacterial drugs.
abstract: There are many of pathogen bacteria species which very different susceptibility profile to different antibacterial drugs. There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Bacteria Pairs (DBPs) of affinity/non-affinity drugs with similar/dissimilar bacteria and represented it as a large network, which may be used to identify drugs that can act on bacteria. Computational chemistry prediction of the biological activity based on one-target Quantitative Structure-Activity Relationship (ot-QSAR) studies substantially increases the potentialities of this kind of networks avoiding time and resource consuming experiments. Unfortunately almost all ot-QSAR models predict the biological activity of drugs against only one bacterial species. Consequently, multi-tasking learning to predict drug's activity against different species with a single model (mt-QSAR) is a goal of major importance. These mt-QSARs offer a good opportunity to construct drug-drug similarity Complex Networks. Unfortunately, almost QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multi-bacteria QSAR classification model. The model correctly classifies 202 out of 241 active compounds (83.8%) and 169 out of 200 non-active cases (84.5%). Overall training predictability was 84.13% (371 out of 441 cases). The validation of the model was carried out by means of external predicting series, classifying the model 197 out of 221 (89.4%) cases. In order to show how the model functions in practice a virtual screening was carried out recognizing the model as active 86.7%, 520 out of 600 cases not used in training or predicting series. Outputs of this QSAR model were used as inputs to construct a network. The observed network has 1242 nodes (DBPs), 772,736 edges or DBPs with similar activity (sDBPs). The network predicted has 1031 nodes, 641,377 sDBPs. After edge-to-edge comparison, we have demonstrated that the predicted network is significantly similar to the observed one and both have distribution closer to exponential than to normal.
authors.affiliation: Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Prado-Prado
authors.forename: Eugenio
authors.initials: E
authors.lastname: Uriarte
authors.forename: Fernanda
authors.initials: F
authors.lastname: Borges
authors.forename: Humberto
authors.initials: H
authors.lastname: González-Díaz
mesh_terms: D000900:Anti-Bacterial Agents / Q000737:chemistry* / Q000494:pharmacology*; D001419:Bacteria / Q000187:drug effects*; D003198:Computer Simulation; D008390:Markov Chains; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000900:Anti-Bacterial Agents