AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Intraflagellar transport protein 172 homolog

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 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.

partner

Reaxense

upacc

Q9UG01

UPID:

IF172_HUMAN

Alternative names:

-

Alternative UPACC:

Q9UG01; A5PKZ0; B2RNU5; Q86X44; Q96HW4; Q9UFJ9; Q9ULP1

Background:

The Intraflagellar transport protein 172 homolog plays a pivotal role in the maintenance and formation of cilia, structures vital for cell movement and signaling. Its involvement in the hedgehog signaling pathway, essential for embryonic development, underscores its biological significance. This protein's function is crucial for the proper operation of various organ systems.

Therapeutic significance:

Linked to diseases such as Short-rib thoracic dysplasia 10, Retinitis pigmentosa 71, and Bardet-Biedl syndrome 20, the Intraflagellar transport protein 172 homolog is at the heart of significant genetic disorders. Understanding its role could open doors to potential therapeutic strategies, especially in tackling ciliopathies and retinal dystrophies.

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