title: Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.
abstract: Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.
authors.affiliation: Lead Discovery Informatics, Lead Discovery Center, Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. James.Nettles@novartis.com
authors.forename: James H
authors.initials: JH
authors.lastname: Nettles
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Jenkins
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
authors.forename: Zhan
authors.initials: Z
authors.lastname: Deng
authors.forename: John W
authors.initials: JW
authors.lastname: Davies
authors.forename: Meir
authors.initials: M
authors.lastname: Glick
mesh_terms: D000255:Adenosine Triphosphate / Q000737:chemistry; D001381:Azepines / Q000737:chemistry; D001665:Binding Sites; D001688:Biological Products / Q000737:chemistry; D017868:Cyclic AMP-Dependent Protein Kinases / Q000737:chemistry; D016208:Databases, Factual; D015195:Drug Design; D062385:Hydroxybenzoates / Q000737:chemistry; D008024:Ligands; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011485:Protein Binding; D011487:Protein Conformation; D011493:Protein Kinase C / Q000737:chemistry; D064546:Protein Kinase C beta; D011506:Proteins / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000737:chemistry; D047488:Retinoid X Receptors / Q000737:chemistry
chemical_list: D001381:Azepines; D001688:Biological Products; D062385:Hydroxybenzoates; D008024:Ligands; D004364:Pharmaceutical Preparations; D011506:Proteins; D011960:Receptors, Estrogen; D047488:Retinoid X Receptors; C015405:ophiocordin; D000255:Adenosine Triphosphate; C081975:protein kinase C eta; D017868:Cyclic AMP-Dependent Protein Kinases; D011493:Protein Kinase C; D064546:Protein Kinase C beta