title: Predicting drug side-effect profiles: a chemical fragment-based approach.
abstract: BACKGROUND
  Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, in silico prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients.
  
  
  RESULTS
  In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information.
  
  
  CONCLUSIONS
  The proposed method is expected to be useful in various stages of the drug development process.
authors.affiliation: Mines ParisTech, Centre for Computational Biology, 35 Rue Saint-Honoré, F-77305 Fontainebleau Cedex, France.
authors.forename: Edouard
authors.initials: E
authors.lastname: Pauwels
authors.forename: Véronique
authors.initials: V
authors.lastname: Stoven
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Yamanishi
mesh_terms: D019295:Computational Biology / Q000191:economics / Q000379:methods*; D016208:Databases, Factual; D016503:Drug Delivery Systems; D015195:Drug Design; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D004364:Pharmaceutical Preparations / Q000737:chemistry
chemical_list: D004364:Pharmaceutical Preparations