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 Coagulation factor VII 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 Coagulation factor VII 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 Coagulation factor VII, 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 Coagulation factor VII. 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 Coagulation factor VII. 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 Coagulation factor VII 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.
Coagulation factor VII
partner:
Reaxense
upacc:
P08709
UPID:
FA7_HUMAN
Alternative names:
Proconvertin; Serum prothrombin conversion accelerator
Alternative UPACC:
P08709; B0YJC8; Q14339; Q5JVF1; Q5JVF2; Q9UD52; Q9UD53; Q9UD54
Background:
Coagulation factor VII, also known as Proconvertin or Serum prothrombin conversion accelerator, plays a pivotal role in initiating the extrinsic pathway of blood coagulation. This serine protease circulates in the blood in a zymogen form and is activated to factor VIIa through minor proteolysis by factor Xa, factor XIIa, factor IXa, or thrombin. Upon activation, in the presence of tissue factor and calcium ions, factor VIIa catalyzes the conversion of factor X to factor Xa and factor IX to factor IXa, facilitating blood clot formation.
Therapeutic significance:
Factor VII deficiency, a hemorrhagic condition with variable presentation, is directly linked to mutations affecting the gene encoding Coagulation factor VII. The disease spectrum ranges from severe, including intracerebral hemorrhages and repeated hemarthroses, to moderate, characterized by cutaneous-mucosal hemorrhages or hemorrhages post-surgical intervention. Understanding the role of Coagulation factor VII could open doors to potential therapeutic strategies for managing this condition.
