AI-ACCELERATED DRUG DISCOVERY

Hepatocyte nuclear factor 4-alpha

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Hepatocyte nuclear factor 4-alpha - Focused Library Design

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 Hepatocyte nuclear factor 4-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 Hepatocyte nuclear factor 4-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 Hepatocyte nuclear factor 4-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 Hepatocyte nuclear factor 4-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 Hepatocyte nuclear factor 4-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 Hepatocyte nuclear factor 4-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.

Hepatocyte nuclear factor 4-alpha

partner:

Reaxense

upacc:

P41235

UPID:

HNF4A_HUMAN

Alternative names:

Nuclear receptor subfamily 2 group A member 1; Transcription factor 14; Transcription factor HNF-4

Alternative UPACC:

P41235; A5JW41; B2RPP8; O00659; O00723; Q14540; Q5QPB8; Q6B4V5; Q6B4V6; Q6B4V7; Q92653; Q92654; Q92655; Q99864; Q9NQH0

Background:

Hepatocyte nuclear factor 4-alpha (HNF-4α), encoded by the gene with accession number P41235, serves as a pivotal transcriptional regulator. It orchestrates the expression of hepatic genes pivotal in the transition from endodermal cells to hepatic progenitors, thereby facilitating RNA polymerase II recruitment. Its roles extend to repressing CLOCK-BMAL1 activity, crucial for circadian rhythm in liver and colon cells.

Therapeutic significance:

HNF-4α is implicated in several metabolic disorders, including Maturity-onset diabetes of the young 1, Type 2 diabetes mellitus, and Fanconi renotubular syndrome 4. These associations underscore its potential as a therapeutic target, offering avenues for novel treatments in diabetes and metabolic syndrome management.

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