AI-ACCELERATED DRUG DISCOVERY

Retinoid-binding protein 7

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Retinoid-binding protein 7 - 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 Retinoid-binding protein 7 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 Retinoid-binding protein 7 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 Retinoid-binding protein 7, 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 Retinoid-binding protein 7. 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 Retinoid-binding protein 7. 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 Retinoid-binding protein 7 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.

Retinoid-binding protein 7

partner:

Reaxense

upacc:

Q96R05

UPID:

RET7_HUMAN

Alternative names:

Cellular retinoic acid-binding protein 4; Cellular retinoic acid-binding protein IV

Alternative UPACC:

Q96R05; B2R517; Q5SWJ4

Background:

Retinoid-binding protein 7, also known as Cellular retinoic acid-binding protein 4 or Cellular retinoic acid-binding protein IV, plays a crucial role in the intracellular transport of retinol. This protein is pivotal in the metabolism and function of retinoids, which are vital for vision, growth, and cellular differentiation.

Therapeutic significance:

Understanding the role of Retinoid-binding protein 7 could open doors to potential therapeutic strategies. Its involvement in the retinol transport system highlights its importance in cellular processes and its potential as a target for drug discovery in diseases where retinoid metabolism and function are compromised.

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