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 Hemoglobin subunit alpha 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 Hemoglobin subunit alpha 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 Hemoglobin subunit alpha, 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 Hemoglobin subunit alpha. 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 Hemoglobin subunit alpha. 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 Hemoglobin subunit alpha 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.
Hemoglobin subunit alpha
partner:
Reaxense
upacc:
P69905
UPID:
HBA_HUMAN
Alternative names:
Alpha-globin; Hemoglobin alpha chain
Alternative UPACC:
P69905; P01922; Q1HDT5; Q3MIF5; Q53F97; Q96KF1; Q9NYR7; Q9UCM0
Background:
Hemoglobin subunit alpha, known alternatively as Alpha-globin or Hemoglobin alpha chain, plays a crucial role in oxygen transport from the lungs to peripheral tissues. Its unique structure facilitates the efficient delivery of oxygen, essential for cellular metabolism and function. Additionally, Hemopressin, a peptide derived from the alpha-globin, acts as an antagonist to the cannabinoid receptor CNR1, blocking its signaling pathways.
Therapeutic significance:
The alpha-globin gene is implicated in several hematological disorders, including Heinz body anemias, Alpha-thalassemia, and Hemoglobin H disease. These conditions range from mild to severe anemias, affecting millions worldwide. Understanding the genetic and molecular basis of these diseases offers potential for targeted gene therapies, improving patient outcomes and quality of life.
