Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We employ our advanced, specialised process to create targeted 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
P43628

UPID:
KI2L3_HUMAN

ALTERNATIVE NAMES:
CD158 antigen-like family member B2; KIR-023GB; Killer inhibitory receptor cl 2-3; NKAT2a; NKAT2b; Natural killer-associated transcript 2; p58 natural killer cell receptor clone CL-6; p58.2 MHC class-I-specific NK receptor

ALTERNATIVE UPACC:
P43628; O43472; P78402; Q14944; Q14945; Q9UM51; Q9UQ70

BACKGROUND:
The protein Killer cell immunoglobulin-like receptor 2DL3, identified by the accession number P43628, serves as a critical checkpoint in the immune system. It inhibits natural killer (NK) cell activity through interaction with specific HLA-C alleles, thus playing a vital role in the modulation of immune responses. Its alternative names, such as NKAT2b and p58.2 MHC class-I-specific NK receptor, reflect its diverse functions and importance in NK cell regulation.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Killer cell immunoglobulin-like receptor 2DL3 offers a promising avenue for the development of novel therapeutic approaches. Given its regulatory role in NK cell-mediated cytotoxicity, targeting KIR2DL3 could provide innovative solutions for enhancing immune responses against malignancies and viral infections.

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