Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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 Reaxense.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


Our high-tech, dedicated method is applied to construct targeted 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
Q9BQL6

UPID:
FERM1_HUMAN

ALTERNATIVE NAMES:
Kindlerin; Kindlin syndrome protein; Kindlin-1; Unc-112-related protein 1

ALTERNATIVE UPACC:
Q9BQL6; D3DW10; Q8IX34; Q8IYH2; Q9NWM2; Q9NXQ3

BACKGROUND:
Fermitin family homolog 1, also referred to as Kindlin-1, is integral to cell adhesion mechanisms and integrin signaling pathways. It supports the structural integrity of skin by regulating keratinocyte adhesion, proliferation, and migration. The protein's ability to mediate TGF-beta 1 signaling is also implicated in tumor progression, highlighting its significance in both developmental and pathological contexts.

THERAPEUTIC SIGNIFICANCE:
The association of Fermitin family homolog 1 with Kindler syndrome, a condition marked by severe skin atrophy and malignancy risk, underscores its therapeutic potential. Exploring the molecular mechanisms of Fermitin family homolog 1 could lead to innovative treatments for skin disorders and possibly influence cancer therapy by targeting its role in tumor progression.

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