title: Feature selection and transduction for prediction of molecular bioactivity for drug design.
abstract: BACKGROUND
  In drug discovery a key task is to identify characteristics that separate active (binding) compounds from inactive (non-binding) ones. An automated prediction system can help reduce resources necessary to carry out this task.
  
  
  RESULTS
  Two methods for prediction of molecular bioactivity for drug design are introduced and shown to perform well in a data set previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001. The data is characterized by very few positive examples, a very large number of features (describing three-dimensional properties of the molecules) and rather different distributions between training and test data. Two techniques are introduced specifically to tackle these problems: a feature selection method for unbalanced data and a classifier which adapts to the distribution of the the unlabeled test data (a so-called transductive method). We show both techniques improve identification performance and in conjunction provide an improvement over using only one of the techniques. Our results suggest the importance of taking into account the characteristics in this data which may also be relevant in other problems of a similar type.
authors.affiliation: Max Planck Institute, Spemannstr. 38, Tuebingen, 72076, Germany. jason.weston@tuebingen.mpg.de
authors.forename: Jason
authors.initials: J
authors.lastname: Weston
authors.forename: Fernando
authors.initials: F
authors.lastname: Pérez-Cruz
authors.forename: Olivier
authors.initials: O
authors.lastname: Bousquet
authors.forename: Olivier
authors.initials: O
authors.lastname: Chapelle
authors.forename: André
authors.initials: A
authors.lastname: Elisseeff
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schölkopf
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D030562:Databases, Protein; D015195:Drug Design; D046911:Macromolecular Substances; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D011955:Receptors, Drug / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D046911:Macromolecular Substances; D011506:Proteins; D011955:Receptors, Drug