TY  - JOUR
AU  - Goßen, Jonas
AU  - Ribeiro, Rui Pedro
AU  - Bier, Dirk
AU  - Neumaier, Bernd
AU  - Carloni, Paolo
AU  - Giorgetti, Alejandro
AU  - Rossetti, Giulia
TI  - AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology
JO  - Chemical science
VL  - 14
IS  - 32
SN  - 2041-6520
CY  - Cambridge
PB  - RSC
M1  - FZJ-2023-02927
SP  - 8651-8661
PY  - 2023
AB  - Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson's disease and cancer. The identified antagonists were tested here in a radio ligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310 ± 23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range
LB  - PUB:(DE-HGF)16
C6  - 37592985
UR  - <Go to ISI:>//WOS:001039483600001
DO  - DOI:10.1039/D3SC02352D
UR  - https://juser.fz-juelich.de/record/1009674
ER  -