AI-ACCELERATED DRUG DISCOVERY

Cannabinoid receptor 1

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Cannabinoid receptor 1 - Focused Library Design

Available from Reaxense

This protein is integrated into the Receptor.AI ecosystem as a prospective target with high therapeutic potential. We performed a comprehensive characterization of Cannabinoid receptor 1 including:

1. LLM-powered literature research

Our custom-tailored LLM extracted and formalized all relevant information about the protein from a large set of structured and unstructured data sources and stored it in the form of a Knowledge Graph. This comprehensive analysis allowed us to gain insight into Cannabinoid receptor 1 therapeutic significance, existing small molecule ligands, relevant off-targets, and protein-protein interactions.

 Fig. 1. Preliminary target research workflow

2. AI-Driven Conformational Ensemble Generation

Starting from the initial protein structure, we employed advanced AI algorithms to predict alternative functional states of Cannabinoid receptor 1, including large-scale conformational changes along "soft" collective coordinates. Through molecular simulations with AI-enhanced sampling and trajectory clustering, we explored the broad conformational space of the protein and identified its representative structures. Utilizing diffusion-based AI models and active learning AutoML, we generated a statistically robust ensemble of equilibrium protein conformations that capture the receptor's full dynamic behavior, providing a robust foundation for accurate structure-based drug design.

 Fig. 2. AI-powered molecular dynamics simulations workflow

3. Binding pockets identification and characterization

We employed the AI-based pocket prediction module to discover orthosteric, allosteric, hidden, and cryptic binding pockets on the protein’s surface. Our technique integrates the LLM-driven literature search and structure-aware ensemble-based pocket detection algorithm that utilizes previously established protein dynamics. Tentative pockets are then subject to AI scoring and ranking with simultaneous detection of false positives. In the final step, the AI model assesses the druggability of each pocket enabling a comprehensive selection of the most promising pockets for further targeting.

 Fig. 3. AI-based binding pocket detection workflow

4. AI-Powered Virtual Screening

Our ecosystem is equipped to perform AI-driven virtual screening on Cannabinoid receptor 1. With access to a vast chemical space and cutting-edge AI docking algorithms, we can rapidly and reliably predict the most promising, novel, diverse, potent, and safe small molecule ligands of Cannabinoid receptor 1. This approach allows us to achieve an excellent hit rate and to identify compounds ready for advanced lead discovery and optimization.

 Fig. 4. The screening workflow of Receptor.AI

Receptor.AI, in partnership with Reaxense, developed a next-generation technology for on-demand focused library design to enable extensive target exploration.

The focused library for Cannabinoid receptor 1 includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

Cannabinoid receptor 1

partner:

Reaxense

upacc:

P21554

UPID:

CNR1_HUMAN

Alternative names:

CANN6

Alternative UPACC:

P21554; B2R9T4; E1P512; Q13949; Q495Z0; Q4PLI4; Q4VBM6; Q5JVL5; Q5UB37; Q9UNN0

Background:

Cannabinoid receptor 1 (CNR1), also known as CANN6, is a G-protein coupled receptor primarily activated by endocannabinoids. It plays a pivotal role in regulating food intake, memory, gastrointestinal motility, and energy metabolism. CNR1's interaction with cannabinoids affects cellular respiration, mitochondrial function, and neurotransmission, showcasing its complex involvement in metabolic and neurological processes.

Therapeutic significance:

CNR1's involvement in obesity highlights its potential as a therapeutic target. Its role in diet-induced obesity, dyslipidemia, and liver steatosis suggests that modulation of CNR1 activity could offer new avenues for treating metabolic disorders. Although CNR1 inverse agonists have shown promise in reducing body weight and metabolic abnormalities, their development has been challenged by adverse effects. Further research into CNR1 could unlock novel, safer therapeutic strategies.

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