AI-ACCELERATED DRUG DISCOVERY
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 11-beta-hydroxysteroid dehydrogenase type 2 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 11-beta-hydroxysteroid dehydrogenase type 2 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 11-beta-hydroxysteroid dehydrogenase type 2, 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 11-beta-hydroxysteroid dehydrogenase type 2. 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 11-beta-hydroxysteroid dehydrogenase type 2. 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 11-beta-hydroxysteroid dehydrogenase type 2 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.
11-beta-hydroxysteroid dehydrogenase type 2
partner:
Reaxense
upacc:
P80365
UPID:
DHI2_HUMAN
Alternative names:
11-beta-hydroxysteroid dehydrogenase type II; Corticosteroid 11-beta-dehydrogenase isozyme 2; NAD-dependent 11-beta-hydroxysteroid dehydrogenase; Short chain dehydrogenase/reductase family 9C member 3
Alternative UPACC:
P80365; A7LB28; C5HTY7; Q13194; Q6P2G9; Q8N439; Q96QN8; Q99887; Q9UC50; Q9UC51; Q9UCW5; Q9UCW6; Q9UCW7; Q9UCW8
Background:
11-beta-hydroxysteroid dehydrogenase type 2 (11β-HSD2) plays a pivotal role in converting active 11β-hydroxysteroids, like cortisol, into their inactive forms, such as cortisone. This enzymatic activity is crucial for maintaining glucocorticoid balance and protecting the mineralocorticoid receptor from glucocorticoid occupation. The protein is also involved in the metabolism of androgens and cholesterol derivatives, impacting immune cell migration and fetal development.
Therapeutic significance:
The dysfunction of 11β-HSD2 is directly linked to Apparent Mineralocorticoid Excess (AME), a condition characterized by severe hypertension and electrolyte imbalances from early life. Understanding the role of 11β-HSD2 could open doors to potential therapeutic strategies for AME and related disorders, offering hope for targeted treatments.
