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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal 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

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast 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
Q7L0Y3

UPID:
TM10C_HUMAN

ALTERNATIVE NAMES:
HBV pre-S2 trans-regulated protein 2; Mitochondrial ribonuclease P protein 1; RNA (guanine-9-)-methyltransferase domain-containing protein 1; Renal carcinoma antigen NY-REN-49; mRNA methyladenosine-N(1)-methyltransferase; tRNA (adenine(9)-N(1))-methyltransferase; tRNA (guanine(9)-N(1))-methyltransferase

ALTERNATIVE UPACC:
Q7L0Y3; Q9NRG5; Q9NX54; Q9Y596

BACKGROUND:
The protein tRNA methyltransferase 10 homolog C, also known as TRMT10C, is integral to mitochondrial RNA processing, including tRNA maturation and mRNA methylation. It forms a critical component of the mitochondrial ribonuclease P complex, facilitating the production of essential proteins and supporting mitochondrial health. Its involvement in methylation processes underscores its significance in genetic expression and cellular energy metabolism.

THERAPEUTIC SIGNIFICANCE:
Given its association with Combined oxidative phosphorylation deficiency 30, a mitochondrial disease characterized by severe metabolic disturbances, TRMT10C represents a promising target for therapeutic intervention. Exploring TRMT10C's function offers potential pathways for developing treatments for mitochondrial disorders, underscoring the protein's therapeutic relevance.

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