Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We utilise our cutting-edge, exclusive workflow to develop focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.


Our library is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q02161

UPID:
RHD_HUMAN

ALTERNATIVE NAMES:
RHXIII; Rh polypeptide 2; Rhesus D antigen

ALTERNATIVE UPACC:
Q02161; Q02162; Q07618; Q16147; Q16235; Q16355; Q5VSK0; Q5XLS9; Q5XLT1; Q5XLT2; Q9NPK0; Q9UQ20; Q9UQ21; Q9UQ22; Q9UQ23

BACKGROUND:
Blood group Rh(D) polypeptide, identified by alternative names such as RHXIII, Rh polypeptide 2, and Rhesus D antigen, is implicated in vital transport or channel functions within the erythrocyte membrane. This protein's involvement in forming an oligomeric complex underscores its significance in maintaining erythrocyte membrane stability and functionality.

THERAPEUTIC SIGNIFICANCE:
In the context of Hemolytic disease of the fetus and newborn, RH-induced, the Blood group Rh(D) polypeptide's malfunction due to genetic variants triggers maternal autoantibodies to destroy fetal red cells. Delving into the Blood group Rh(D) polypeptide's function offers a promising avenue for devising innovative therapeutic interventions to combat this disease.

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