Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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 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 top-notch dedicated system is used to design specialised 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 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
Q86XF0

UPID:
DYR2_HUMAN

ALTERNATIVE NAMES:
Dihydrofolate reductase, mitochondrial; Dihydrofolate reductase-like protein 1

ALTERNATIVE UPACC:
Q86XF0; D3DN30; Q6P4I9

BACKGROUND:
The enzyme Dihydrofolate reductase 2, mitochondrial, with alternative names such as Dihydrofolate reductase-like protein 1, is integral to folate metabolism. It supports the mitochondrial pathway for thymidylate biosynthesis, necessary for DNA synthesis and repair, and prevents uracil accumulation in mtDNA. It also has a role in mRNA regulation for itself and DHFR.

THERAPEUTIC SIGNIFICANCE:
Exploring the function of Dihydrofolate reductase 2, mitochondrial reveals potential avenues for therapeutic intervention. Given its essential role in maintaining folate metabolism and mitochondrial DNA integrity, targeting this enzyme could lead to novel treatments for mitochondrial disorders and enhance our understanding of folate-related metabolic pathways.

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