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 Runt-related transcription factor 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 Runt-related transcription factor 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 Runt-related transcription factor 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 Runt-related transcription factor 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 Runt-related transcription factor 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 Runt-related transcription factor 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.
Runt-related transcription factor 1
partner:
Reaxense
upacc:
Q01196
UPID:
RUNX1_HUMAN
Alternative names:
Acute myeloid leukemia 1 protein; Core-binding factor subunit alpha-2; Oncogene AML-1; Polyomavirus enhancer-binding protein 2 alpha B subunit; SL3-3 enhancer factor 1 alpha B subunit; SL3/AKV core-binding factor alpha B subunit
Alternative UPACC:
Q01196; A8MV94; B2RMS4; D3DSG1; O60472; O60473; O76047; O76089; Q13081; Q13755; Q13756; Q13757; Q13758; Q13759; Q15341; Q15343; Q16122; Q16284; Q16285; Q16286; Q16346; Q16347; Q92479
Background:
Runt-related transcription factor 1 (RUNX1) plays a pivotal role in hematopoiesis, acting as a core component of the core-binding factor (CBF) complex. It modulates transcription by recognizing specific DNA sequences, essential for normal blood cell development. RUNX1's interaction with various proteins, including CBFB and ELF4, underscores its multifaceted role in gene regulation and lineage commitment of T cells.
Therapeutic significance:
RUNX1's mutation is linked to Familial platelet disorder with associated myeloid malignancy, highlighting its critical role in blood diseases. Understanding RUNX1's function could pave the way for innovative treatments targeting hematological disorders and leukemia.
