AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Intraflagellar transport protein 27 homolog

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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 our partner 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.

We employ our advanced, specialised process to create targeted 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.

partner

Reaxense

upacc

Q9BW83

UPID:

IFT27_HUMAN

Alternative names:

Putative GTP-binding protein RAY-like; Rab-like protein 4

Alternative UPACC:

Q9BW83; O60897

Background:

Intraflagellar transport protein 27 homolog (IFT27), also known as Putative GTP-binding protein RAY-like and Rab-like protein 4, plays a crucial role in ciliary function. It is a component of the intraflagellar transport (IFT) complex B, essential for cilia exit of the BBSome complex, hedgehog signaling, and sperm flagella formation. IFT27's involvement in kidney and testis development highlights its importance in cellular structures rich in ciliated cells.

Therapeutic significance:

IFT27's association with Bardet-Biedl syndrome 19, a genetic disorder characterized by severe symptoms including pigmentary retinopathy and obesity, underscores its therapeutic potential. Understanding the role of IFT27 could open doors to potential therapeutic strategies for this syndrome and related ciliopathies.

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