In silico drug discovery: solving the "target-rich and lead-poor" imbalance using the genome-to-drug-lead paradigm.
Advances in genomics, proteomics, and structural genomics have identified a large number of protein targets. Virtual screening has gained popularity in identifying drug leads by computationally screening large numbers of chemicals against experimentally determined protein targets. In that context, there continues to be a "target-rich and lead-poor" imbalance, reflecting an insufficiency of chemists pursuing drug discovery in academia, the challenge of engaging more chemists in this area of research, and a paucity of available protein target structures. This imbalance in manpower and structural information can be ameliorated, in part, by adapting a "genome-to-drug-lead" approach, in which chemicals can be virtually screened against computer-predicted protein targets, within the context of the US National Science Foundation's petascale computing initiative. This approach offers a solution to reduce manpower requirements for more chemists to experimentally search for drug leads, which represent one of the greatest limitations to drug discovery and better exploits the extensive availability of drug targets at the gene level, ultimately improving the success of moving discoveries from the laboratory to the patient.
Pang
Y P
YP
Computer-Aided Molecular Design Laboratory, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. pang@mayo.edu
D002626:Chemistry, Pharmaceutical / Q000295:instrumentation / Q000458:organization & administration*; D019295:Computational Biology; D015195:Drug Design; D015894:Genome, Human; D006801:Humans; D008958:Models, Molecular; D010597:Pharmacogenetics; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry
D011506:Proteins