Computational approaches to model ligand selectivity in drug design.
To be effective, a designed drug must discriminate successfully the macromolecular target from alternative structures present in the organism. The last few years have witnessed the emergence of different computational tools aimed to the understanding and modeling of this process at molecular level. Although still rudimentary, these methods are shaping a coherent approach to help in the design of molecules with high affinity and specificity, both in lead discovery and in lead optimization. It is the purpose of this review to illustrate the array of computational tools available to consider selectivity in the design process, to summarize the most relevant applications, and to sketch the challenges ahead.
Ortiz
Angel R
AR
Bioinformatics Unit, Centro de Biologia Molecular Severo Ochoa (CSIC-UAM) Cantoblanco, 28049 Madrid, Spain. aro@cbm.uam.es
Gomez-Puertas
Paulino
P
Leo-Macias
Alejandra
A
Lopez-Romero
Pedro
P
Lopez-Viñas
Eduardo
E
Morreale
Antonio
A
Murcia
Marta
M
Wang
Kun
K
D000595:Amino Acid Sequence; D002626:Chemistry, Pharmaceutical; D002795:Choline O-Acetyltransferase / Q000737:chemistry / Q000378:metabolism; D017076:Computer-Aided Design; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011494:Protein Kinases / Q000737:chemistry / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012697:Serine Endopeptidases / Q000737:chemistry / Q000378:metabolism; D013379:Substrate Specificity
D004791:Enzyme Inhibitors; D008024:Ligands; D002795:Choline O-Acetyltransferase; D011494:Protein Kinases; D012697:Serine Endopeptidases