title: Classification of small molecules by two- and three-dimensional decomposition kernels.
abstract: BACKGROUND
  Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on 2D representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit 3D information.
  
  
  RESULTS
  We introduce new ideas for building kernels on small molecules that can effectively use and combine 2D and 3D information. We tested these kernels in conjunction with support vector machines for binary classification on the 60 NCI cancer screening datasets as well as on the NCI HIV data set. Our results show that 3D information leveraged by these kernels can consistently improve prediction accuracy in all datasets.
  
  
  BACKGROUND
  An implementation of the small molecule classifier is available from http://www.dsi.unifi.it/neural/src/3DDK.
authors.affiliation: Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy.
authors.forename: Alessio
authors.initials: A
authors.lastname: Ceroni
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Costa
authors.forename: Paolo
authors.initials: P
authors.lastname: Frasconi
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D014408:Biomarkers, Tumor / Q000737:chemistry*; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009363:Neoplasm Proteins / Q000737:chemistry* / Q000145:classification / Q000648:ultrastructure*; D009369:Neoplasms / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods; D011487:Protein Conformation; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins