Focused On-demand Library for Troponin T, fast skeletal muscle

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, 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.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening 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.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.







Alternative names:

Beta-TnTF; Fast skeletal muscle troponin T

Alternative UPACC:

P45378; A8MQ76; A8MSW1; B3KPX3; B7WP64; B7ZL26; B7ZVV9; Q12975; Q12976; Q12977; Q12978; Q17RG9; Q6FH29; Q6N056; Q86TH6


Troponin T, fast skeletal muscle (P45378), also known as Beta-TnTF, plays a pivotal role in muscle contraction. It serves as the tropomyosin-binding subunit of troponin, a key component in the thin filament regulatory complex. This complex is crucial for conferring calcium-sensitivity to the actomyosin ATPase activity in striated muscles, facilitating coordinated muscle movements.

Therapeutic significance:

The protein is linked to Arthrogryposis, distal, 2B2, a condition marked by congenital joint contractures. Understanding the role of Troponin T, fast skeletal muscle could open doors to potential therapeutic strategies for this autosomal dominant disease.

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