title: Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals.
abstract: Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.
authors.affiliation: REQUIMTE, Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal. alejspivanovich@gmail.com
authors.forename: Alejandro
authors.initials: A
authors.lastname: Speck-Planche
authors.forename: Valeria V
authors.initials: VV
authors.lastname: Kleandrova
authors.forename: M N D S
authors.initials: MN
authors.lastname: Cordeiro
mesh_terms: D000818:Animals; D000830:Animals, Laboratory; D000900:Anti-Bacterial Agents / Q000737:chemistry* / Q000494:pharmacology*; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods; D006801:Humans; D048088:Informatics / Q000379:methods; D008954:Models, Biological; D021281:Quantitative Structure-Activity Relationship; D013291:Streptococcus / Q000737:chemistry / Q000187:drug effects; D013329:Structure-Activity Relationship
chemical_list: D000900:Anti-Bacterial Agents