title: Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents.
abstract: BACKGROUND
  Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET).
  
  
  RESULTS
  A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted.
  
  
  CONCLUSIONS
  Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.
authors.affiliation: REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Speck-Planche
authors.forename: Maria Natália Dias Soeiro
authors.initials: MN
authors.lastname: Cordeiro
mesh_terms: D000900:Anti-Bacterial Agents / Q000737:chemistry* / Q000378:metabolism / Q000494:pharmacology; D019540:Area Under Curve; D002626:Chemistry, Pharmaceutical; D004353:Drug Evaluation, Preclinical; D024841:Fluoroquinolones / Q000737:chemistry / Q000378:metabolism / Q000494:pharmacology; D006094:Gram-Positive Bacteria / Q000187:drug effects; D008826:Microbial Sensitivity Tests; D008956:Models, Chemical; D012372:ROC Curve
chemical_list: C550935:7-(3-(2-amino-1-fluoroethylidene)-1-piperidinyl)-1-cyclopropyl-6-fluoro-1,4-dihydro-8-methoxy-4-oxo-3-quinolinecarboxylic acid; D000900:Anti-Bacterial Agents; D024841:Fluoroquinolones