AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Twinfilin-1

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.

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.

We use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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

Reaxense

upacc

Q12792

UPID:

TWF1_HUMAN

Alternative names:

Protein A6; Protein tyrosine kinase 9

Alternative UPACC:

Q12792; A8K5A8; B3KXS6; B4DLX9; Q59G07; Q5U0B1; Q6FHJ1; Q6FHL6; Q6NUK9; Q86XL6; Q8TCD3

Background:

Twinfilin-1, also known as Protein A6 and Protein tyrosine kinase 9, is a pivotal actin-binding protein that plays a crucial role in cellular motility and morphology. It functions by inhibiting actin polymerization, primarily through sequestering G-actin, and by capping the barbed ends of filaments, thereby regulating cell motility. Twinfilin-1 is also implicated in clathrin-mediated endocytosis and the distribution of endocytic organelles, highlighting its significance in cellular dynamics.

Therapeutic significance:

Understanding the role of Twinfilin-1 could open doors to potential therapeutic strategies. Its involvement in critical cellular processes such as actin polymerization and endocytosis positions it as a key target for drug discovery efforts aimed at modulating cellular movement and morphology for therapeutic benefit.

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