AI-based novel-chemotype GPCRs drugs: introducing ligand type classifiers and systems biology
Gossen, J.; Ribeiro, R. P.; Bier, D.; Neumaier, B.; Carloni, P.; Giorgetti, A.; Rossetti, G.
Show abstract
Identifying the correct chemotype of ligands targeting receptors (i.e., agonist or antagonist) 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 Parkinsons disease and cancer. The identified antagonists were tested here in a radioligand 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.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces 96%
- Deep Generative Design with 3D Pharmacophoric Constraints 96%
- Structure-Aware Dual-Target Drug Design through Collaborative Learning of Pharmacophore Combination and Molecular Simulation 95%
Similar papers in this journal
Similar papers in this journal
- Thinking like a structural biologist: A pocket-based 3D molecule generative model fueled by electron density 95%
- From atoms to cells: bridging the gap between potency, efficacy, and safety of small molecules directed at a membrane protein 94%
- HTRF-based identification of small molecules targeting SARS-CoV-2 E protein interaction with ZO-1 PDZ2 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.