Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We employ our advanced, specialised process to create targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.


Our library distinguishes itself through several key aspects:


  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.

  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.

  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.

  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q15366

UPID:
PCBP2_HUMAN

ALTERNATIVE NAMES:
Alpha-CP2; Heterogeneous nuclear ribonucleoprotein E2

ALTERNATIVE UPACC:
Q15366; A8K7X6; B4DXP5; F8VYL7; G3V0E8; I6L8F9; Q32Q82; Q59HD4; Q68Y55; Q6IPF4; Q6PKG5

BACKGROUND:
Poly(rC)-binding protein 2, identified by its aliases Alpha-CP2 and Heterogeneous nuclear ribonucleoprotein E2, binds preferentially to oligo dC. As a major cellular poly(rC)-binding protein, it serves as a negative regulator of antiviral signaling pathways, including the MAVS signaling and cGAS-STING pathway. Its interaction with MAVS and the E3 ubiquitin ligase ITCH triggers MAVS ubiquitination and degradation. The protein's involvement in erythropoiesis and its role in viral RNA replication highlight its multifunctional nature.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Poly(rC)-binding protein 2 could open doors to potential therapeutic strategies.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.