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 Angiogenic factor with G patch and FHA domains 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 Angiogenic factor with G patch and FHA domains 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 Angiogenic factor with G patch and FHA domains 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 Angiogenic factor with G patch and FHA domains 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 Angiogenic factor with G patch and FHA domains 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 Angiogenic factor with G patch and FHA domains 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.
Angiogenic factor with G patch and FHA domains 1
partner:
Reaxense
upacc:
Q8N302
UPID:
AGGF1_HUMAN
Alternative names:
Angiogenic factor VG5Q; G patch domain-containing protein 7; Vasculogenesis gene on 5q protein
Alternative UPACC:
Q8N302; O00581; Q53YS3; Q9BU84; Q9NW66
Background:
Angiogenic factor with G patch and FHA domains 1, also known as Angiogenic factor VG5Q, plays a pivotal role in promoting angiogenesis and endothelial cell proliferation. Its ability to bind to endothelial cells suggests an autocrine mechanism of action, enhancing vascular formation and growth.
Therapeutic significance:
Linked to Klippel-Trenaunay syndrome, a congenital disease marked by vascular malformations and tissue hypertrophy, this protein's gene variants are crucial in disease manifestation. Targeting its pathway offers a promising avenue for therapeutic intervention in vascular anomalies.
