Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


Our high-tech, dedicated method is applied to construct targeted 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
Q8N983

UPID:
RM43_HUMAN

ALTERNATIVE NAMES:
39S ribosomal protein L43, mitochondrial; Mitochondrial ribosomal protein bMRP36a

ALTERNATIVE UPACC:
Q8N983; B1AL06; B1AL07; B1AL09; B1AL10; C9J5Q3; D3DR71; Q5JW06; Q7Z719; Q7Z7H6; Q86XN1; Q9BYC7

BACKGROUND:
Large ribosomal subunit protein mL43, with alternative names such as 39S ribosomal protein L43, mitochondrial, and mitochondrial ribosomal protein bMRP36a, is integral to mitochondrial protein synthesis. This protein is a component of the mitochondrial ribosome, where it is involved in translating mitochondrial DNA-encoded proteins, crucial for cellular respiration and energy production.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Large ribosomal subunit protein mL43 offers a promising pathway to novel therapeutic interventions. Given its essential role in mitochondrial biogenesis and function, targeting this protein could provide new avenues for the treatment of metabolic and mitochondrial disorders.

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.