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 Proto-oncogene tyrosine-protein kinase receptor Ret 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 Proto-oncogene tyrosine-protein kinase receptor Ret 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 Proto-oncogene tyrosine-protein kinase receptor Ret, 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 Proto-oncogene tyrosine-protein kinase receptor Ret. 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 Proto-oncogene tyrosine-protein kinase receptor Ret. 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 Proto-oncogene tyrosine-protein kinase receptor Ret 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.
Proto-oncogene tyrosine-protein kinase receptor Ret
partner:
Reaxense
upacc:
P07949
UPID:
RET_HUMAN
Alternative names:
Cadherin family member 12; Proto-oncogene c-Ret
Alternative UPACC:
P07949; A8K6Z2; Q15250; Q9BTB0; Q9H4A2
Background:
The Proto-oncogene tyrosine-protein kinase receptor Ret, also known as Cadherin family member 12, plays a pivotal role in cell proliferation, neuronal navigation, cell migration, and differentiation. It is crucial for organogenesis, including the development of the enteric nervous system and kidneys, and modulates cell adhesion and migration in an integrin-dependent manner.
Therapeutic significance:
Ret's involvement in diseases such as Colorectal cancer, Hirschsprung disease 1, Medullary thyroid carcinoma, Multiple neoplasia 2B, Pheochromocytoma, and Multiple neoplasia 2A highlights its potential as a therapeutic target. Understanding Ret's role could lead to novel treatments for these conditions.
