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.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


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 is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q00872

UPID:
MYPC1_HUMAN

ALTERNATIVE NAMES:
C-protein, skeletal muscle slow isoform

ALTERNATIVE UPACC:
Q00872; B4DKR5; B7Z8G8; B7ZL02; B7ZL09; B7ZL10; E7ESM5; E7EWS6; G3XAE8; Q15497; Q17RR7; Q569K7; Q86T48; Q86TC8; Q8N3L2

BACKGROUND:
The Myosin-binding protein C, slow-type, known alternatively as C-protein, skeletal muscle slow isoform, is integral to muscle function. It associates with myosin and actin within vertebrate striated muscle, influencing the actin-activated myosin ATPase activity. This interaction is crucial for muscle contraction and structural integrity.

THERAPEUTIC SIGNIFICANCE:
Given its association with conditions like Arthrogryposis, distal, 1B, Lethal congenital contracture syndrome 4, and Congenital myopathy 16, the therapeutic potential of Myosin-binding protein C, slow-type is significant. Exploring its function further could lead to novel therapeutic approaches for these debilitating muscular diseases.

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