Focused On-demand Libraries - Receptor.AI 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 Reaxense.


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


Our top-notch dedicated system is used to design specialised 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 distinguishes itself through several key aspects:


  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.

  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.

  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.

  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q14807

UPID:
KIF22_HUMAN

ALTERNATIVE NAMES:
Kinesin-like DNA-binding protein; Kinesin-like protein 4

ALTERNATIVE UPACC:
Q14807; B2R5M0; B7Z265; O60845; O94814; Q53F58; Q9BT46

BACKGROUND:
The Kinesin-like protein KIF22, alternatively named Kinesin-like DNA-binding protein, is integral to chromosome segregation during cell division. It binds microtubules and DNA, facilitating spindle formation and chromosome movements in mitosis and meiosis. Its role in congression of laterally attached chromosomes highlights its importance in maintaining genomic integrity.

THERAPEUTIC SIGNIFICANCE:
Linked to the bone disorder Spondyloepimetaphyseal dysplasia with joint laxity, 2, Kinesin-like protein KIF22's dysfunction underscores its potential as a therapeutic target. Exploring KIF22's mechanisms could lead to innovative treatments for this and potentially other genetic diseases, emphasizing the protein's therapeutic significance.

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