Virtual fragment screening: Discovery of histamine H₃ receptor ligands using ligand-based and protein-based molecular fingerprints

TY - JOUR

T1 - Virtual fragment screening

T2 - Discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints

AU - Sirci, Francesco

AU - Istyastono, Enade P.

AU - Istyastono, Enade P.

AU - Vischer, Henry F.

AU - Kooistra, Albert J.

AU - Nijmeijer, Saskia

AU - Kuijer, Martien

AU - Wijtmans, Maikel

AU - Mannhold, Raimund

AU - Leurs, Rob

AU - De Esch, Iwan J.P.

AU - De Graaf, Chris

PY - 2012/12/21

Y1 - 2012/12/21

N2 - Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active and inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using molecular fingerprints derived from a unique set of fragment affinity data for the histamine H3 receptor (H3R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H3R binders from inactive molecules were successfully used for the identification of new H3R ligands. Prospective virtual screening of 156 090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H 3R ligands that offer new potential starting points for the design of H3R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein-fragment interaction space in silico.

AB - Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active and inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using molecular fingerprints derived from a unique set of fragment affinity data for the histamine H3 receptor (H3R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H3R binders from inactive molecules were successfully used for the identification of new H3R ligands. Prospective virtual screening of 156 090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H 3R ligands that offer new potential starting points for the design of H3R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein-fragment interaction space in silico.

UR - http://www.scopus.com/inward/record.url?scp=84871596792&partnerID=8YFLogxK

U2 - 10.1021/ci3004094

DO - 10.1021/ci3004094

M3 - Journal article

C2 - 23140085

AN - SCOPUS:84871596792

SN - 1549-9596

VL - 52

SP - 3308

EP - 3324

JO - Journal of Chemical Information and Modeling

JF - Journal of Chemical Information and Modeling

IS - 12

ER -