Focused On-demand Library for Small ribosomal subunit protein RACK1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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 our associate Reaxense.

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.

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

 Fig. 1. The sreening 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.







Alternative names:

Cell proliferation-inducing gene 21 protein; Guanine nucleotide-binding protein subunit beta-2-like 1; Guanine nucleotide-binding protein subunit beta-like protein 12.3; Human lung cancer oncogene 7 protein; Receptor for activated C kinase; Receptor of activated protein C kinase 1

Alternative UPACC:

P63244; B3KTJ0; D3DWS0; P25388; P99049; Q53HU2; Q5J8M6; Q5VLR4; Q6FH47


Small ribosomal subunit protein RACK1, encoded by the gene P63244, serves as a scaffolding protein, crucial for the recruitment and regulation of various signaling molecules. It interacts with a broad spectrum of proteins, influencing cellular processes such as translational repression, ribosome quality control, and protein kinase C stabilization. RACK1's involvement extends to inhibiting SRC kinases, modulating cell cycle progression, and enhancing apoptosis through BAX oligomerization. Additionally, it plays a pivotal role in microbial infections by interacting with pathogens like Y.pseudotuberculosis, enhancing HIV-1 Nef phosphorylation, and facilitating poxvirus mRNA translation.

Therapeutic significance:

Understanding the role of Small ribosomal subunit protein RACK1 could open doors to potential therapeutic strategies.

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