AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Tripartite motif-containing protein 67

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal 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 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.

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

Q6ZTA4

UPID:

TRI67_HUMAN

Alternative names:

TRIM9-like protein

Alternative UPACC:

Q6ZTA4; Q5TER7; Q5TER8; Q7Z4K7

Background:

Tripartite motif-containing protein 67, also known as TRIM9-like protein, plays a crucial role in cellular processes. Its unique structure, characterized by the tripartite motif, enables it to participate in diverse biological functions. The protein's involvement in cellular mechanisms is a subject of ongoing research, highlighting its importance in understanding cellular biology.

Therapeutic significance:

Understanding the role of Tripartite motif-containing protein 67 could open doors to potential therapeutic strategies. Its pivotal role in cellular processes makes it a promising target for drug discovery, aiming to modulate its function for therapeutic benefits. The exploration of its therapeutic potential is in its infancy, offering a fertile ground for innovative treatments.

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