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.


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 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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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
Q9BTW9

UPID:
TBCD_HUMAN

ALTERNATIVE NAMES:
Beta-tubulin cofactor D; SSD-1; Tubulin-folding cofactor D

ALTERNATIVE UPACC:
Q9BTW9; O95458; Q7L8K1; Q8IXP6; Q8NAX0; Q8WYH4; Q96E74; Q9UF82; Q9UG46; Q9Y2J3

BACKGROUND:
Tubulin-specific chaperone D, known alternatively as SSD-1 or Tubulin-folding cofactor D, is implicated in the tubulin folding pathway, crucial for microtubule assembly. It modulates microtubule dynamics, essential for cellular processes like mitosis and neuron morphogenesis. This protein's interaction with ARL2 and its role in microtubule disruption and epithelial cell detachment underscore its importance in cellular integrity and signaling.

THERAPEUTIC SIGNIFICANCE:
The association of Tubulin-specific chaperone D with a severe autosomal recessive disease characterized by neurodevelopmental and neurodegenerative features underscores its therapeutic significance. Exploring its function and mechanisms could lead to novel interventions for diseases involving cortical atrophy, hypomyelination, and intellectual disability.

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