title: Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds.
abstract: Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.
authors.affiliation: Department of Bioinformatics, CINVESTAV, LANGEBIO, Irapuato 629 36500, Mexico.
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Prado-Prado
authors.forename: Humberto
authors.initials: H
authors.lastname: González-Díaz
authors.forename: Octavio Martinez
authors.initials: OM
authors.lastname: de la Vega
authors.forename: Florencio M
authors.initials: FM
authors.lastname: Ubeira
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000818:Animals; D000981:Antiprotozoal Agents / Q000737:chemistry* / Q000627:therapeutic use*; D003198:Computer Simulation; D016208:Databases, Factual; D015195:Drug Design; D004351:Drug Resistance; D007893:Leishmania donovani / Q000187:drug effects; D007894:Leishmania mexicana / Q000187:drug effects; D008390:Markov Chains; D008956:Models, Chemical; D010963:Plasmodium falciparum / Q000187:drug effects; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D013045:Species Specificity; D018511:Systems Integration; D014346:Trypanosoma brucei brucei / Q000187:drug effects
chemical_list: D000981:Antiprotozoal Agents