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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


Our high-tech, dedicated method is applied to construct 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 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
O75154

UPID:
RFIP3_HUMAN

ALTERNATIVE NAMES:
Arfophilin-1; EF hands-containing Rab-interacting protein; MU-MB-17.148

ALTERNATIVE UPACC:
O75154; B0QYI8; B0QYT8; B1AHQ0; B4DEI7; B4DZR6; Q4VXV7; Q7Z5E9; Q9H155; Q9H1G0; Q9NUI0

BACKGROUND:
The protein Rab11 family-interacting protein 3, alternatively named EF hands-containing Rab-interacting protein, is essential for membrane delivery and cytokinesis abscission. It acts as an 'address tag' for recycling endosome membranes during cytokinesis and is vital for the endosomal recycling compartment's integrity. By regulating the actin cytoskeleton, it plays a role in breast cancer cell motility and activates dynein processivity, crucial for cellular transport mechanisms.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Rab11 family-interacting protein 3 offers a promising avenue for developing novel therapeutic approaches.

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