AI-ACCELERATED DRUG DISCOVERY

Vitamin D3 receptor

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Vitamin D3 receptor - 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 Vitamin D3 receptor 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 Vitamin D3 receptor 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 Vitamin D3 receptor, 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 Vitamin D3 receptor. 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 Vitamin D3 receptor. 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 Vitamin D3 receptor 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.

Vitamin D3 receptor

partner:

Reaxense

upacc:

P11473

UPID:

VDR_HUMAN

Alternative names:

1,25-dihydroxyvitamin D3 receptor; Nuclear receptor subfamily 1 group I member 1

Alternative UPACC:

P11473; B2R5Q1; G3V1V9; Q5PSV3

Background:

The Vitamin D3 receptor, also known as the 1,25-dihydroxyvitamin D3 receptor or Nuclear receptor subfamily 1 group I member 1, is a pivotal nuclear receptor that mediates the effects of vitamin D3. It functions by binding to vitamin D3, entering the nucleus, and forming heterodimers with the retinoid X receptor. These complexes then bind to DNA, activating the transcription of genes involved in calcium homeostasis and response to vitamin D3.

Therapeutic significance:

The Vitamin D3 receptor plays a crucial role in Rickets vitamin D-dependent 2A, a disorder characterized by severe rickets, hypocalcemia, and secondary hyperparathyroidism, often accompanied by total alopecia. Understanding the role of the Vitamin D3 receptor could open doors to potential therapeutic strategies for this and related disorders.

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