AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Kinesin-like protein KIFC1

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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated 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 use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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

Q9BW19

UPID:

KIFC1_HUMAN

Alternative names:

Kinesin-like protein 2; Kinesin-related protein HSET

Alternative UPACC:

Q9BW19; O60887; Q14834; Q4KMP0; Q5SU09; Q6GMS7; Q6P4A5; Q9UQP7

Background:

Kinesin-like protein KIFC1, also known as Kinesin-like protein 2 and Kinesin-related protein HSET, plays a crucial role in cell division by ensuring the proper formation of bipolar spindles, a process vital for chromosome segregation. Additionally, it is implicated in the movement of early endocytic vesicles and the regulation of cilium formation and structure, highlighting its multifunctionality in cellular dynamics.

Therapeutic significance:

Understanding the role of Kinesin-like protein KIFC1 could open doors to potential therapeutic strategies. Its involvement in critical cellular processes such as spindle formation and cilium structure regulation makes it a promising target for drug discovery, aiming to address diseases with underlying cell division or ciliary dysfunctions.

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