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.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


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 use our state-of-the-art dedicated workflow for designing focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q86YR7

UPID:
MF2L2_HUMAN

ALTERNATIVE NAMES:
Dbs-related Rho family guanine nucleotide exchange factor; MCF2-transforming sequence-like protein 2

ALTERNATIVE UPACC:
Q86YR7; O94942; Q6P2B8; Q6ZVJ5; Q8N318

BACKGROUND:
MCF2L2, known for its probable function as a guanine nucleotide exchange factor, is implicated in essential cellular signaling mechanisms. It is alternatively named Dbs-related Rho family guanine nucleotide exchange factor and MCF2-transforming sequence-like protein 2, highlighting its significance in cellular dynamics.

THERAPEUTIC SIGNIFICANCE:
The association of MCF2L2 with Type 2 diabetes mellitus underscores its therapeutic potential. This condition, characterized by insulin resistance and a cluster of metabolic abnormalities, could benefit from targeted interventions focusing on MCF2L2, offering new avenues for treatment and management of the disease.

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