Focused On-demand Libraries - Receptor.AI 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.


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 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.


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


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.


PARTNER
Receptor.AI
 
UPACC
Q15058

UPID:
KIF14_HUMAN

ALTERNATIVE NAMES:
-

ALTERNATIVE UPACC:
Q15058; Q14CI8; Q4G0A5; Q5T1W3

BACKGROUND:
The Kinesin-like protein KIF14 binds to microtubules, facilitating cell division, cytokinesis, and apoptosis. It targets the central spindle and midbody during cytokinesis through interactions with PRC1 and CIT. KIF14 regulates cell growth by controlling cell cycle progression and cytokinesis, affecting the degradation of CDKN1B and the regulation of cyclins. Additionally, it plays a crucial role in late neurogenesis, influencing the development of the cerebellar, cerebral cortex, and olfactory bulb by regulating apoptosis and cell proliferation.

THERAPEUTIC SIGNIFICANCE:
Given KIF14's involvement in Meckel syndrome 12 and Microcephaly 20, targeting this protein could offer new avenues for therapeutic intervention. The exploration of Kinesin-like protein KIF14's function presents an exciting opportunity for developing novel treatments for a range of developmental and neurological conditions.

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