title: Predicting protein druggability.
abstract: The ability to predict whether a particular protein can bind with high affinity and specificity to small, drug-like compounds based solely on its 3D structure has been a longstanding goal of structural biologists and computational scientists. The promise is that an accurate prediction of protein druggability can capitalize on the huge investments already made in structural genomics initiatives by identifying highly druggable proteins and using this information in target identification and validation campaigns. Here we discuss the potential utility of tools that characterize protein targets and describe strategies for the optimal integration of protein druggability data with bioinformatic approaches to target selection.
authors.affiliation: Pharmaceutical Discovery Division GPRD, Abbott Laboratories, R46Y, AP-10, 100 Abbott Park Road, Abbott Park, IL 60064-3500 USA. philip.hajduk@abbott.com
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Hajduk
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Huth
authors.forename: Christin
authors.initials: C
authors.lastname: Tse
mesh_terms: D001665:Binding Sites; D019295:Computational Biology; D015195:Drug Design; D023281:Genomics; D008024:Ligands; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D008024:Ligands; D011506:Proteins