Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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.


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.


We use our state-of-the-art dedicated workflow for designing focused 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 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
P61326

UPID:
MGN_HUMAN

ALTERNATIVE NAMES:
-

ALTERNATIVE UPACC:
P61326; B1ARP8; B2R5A2; O35169; P50606; Q5SW69

BACKGROUND:
The Protein mago nashi homolog, pivotal in pre-mRNA splicing and a core component of the exon junction complex (EJC), is involved in various stages of mRNA metabolism. It ensures the precise marking of exon-exon junctions in mature mRNA, affecting nuclear mRNA export, subcellular localization, and translation efficiency. By inhibiting EIF4A3's ATPase activity in a heterodimer with RBM8A, it stabilizes the EJC on spliced mRNA, facilitating translation enhancement by recruiting mRNAs to the ribosomal 48S preinitiation complex.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Protein mago nashi homolog 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.