Molecular interaction fingerprint approaches for GPCR drug discovery
Research output: Contribution to journal › Review › Research › peer-review
Standard
Molecular interaction fingerprint approaches for GPCR drug discovery. / Vass, Márton; Kooistra, Albert J.; Ritschel, Tina; Leurs, Rob; de Esch, Iwan JP; de Graaf, Chris.
In: Current Opinion in Pharmacology, Vol. 30, 01.10.2016, p. 59-68.Research output: Contribution to journal › Review › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Molecular interaction fingerprint approaches for GPCR drug discovery
AU - Vass, Márton
AU - Kooistra, Albert J.
AU - Ritschel, Tina
AU - Leurs, Rob
AU - de Esch, Iwan JP
AU - de Graaf, Chris
PY - 2016/10/1
Y1 - 2016/10/1
N2 - Protein–ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein–ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein–ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.
AB - Protein–ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein–ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein–ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.
UR - http://www.scopus.com/inward/record.url?scp=84979870754&partnerID=8YFLogxK
U2 - 10.1016/j.coph.2016.07.007
DO - 10.1016/j.coph.2016.07.007
M3 - Review
C2 - 27479316
AN - SCOPUS:84979870754
VL - 30
SP - 59
EP - 68
JO - Current Opinion in Pharmacology
JF - Current Opinion in Pharmacology
SN - 1471-4892
ER -
ID: 199352356