Focused On-demand Library for E3 ubiquitin-protein ligase RNF170

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

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

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.







Alternative names:

Putative LAG1-interacting protein; RING finger protein 170; RING-type E3 ubiquitin transferase RNF170

Alternative UPACC:

Q96K19; D3DSY6; E9PIL4; Q7Z483; Q86YC0; Q8IXR7; Q8N2B5; Q8N5G9; Q8NG30; Q9H0V6


E3 ubiquitin-protein ligase RNF170, also known as Putative LAG1-interacting protein and RING finger protein 170, plays a pivotal role in the ubiquitination and degradation of the inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) via the endoplasmic reticulum-associated degradation (ERAD) pathway. This process is crucial for regulating intracellular calcium levels and maintaining cellular homeostasis.

Therapeutic significance:

RNF170 is implicated in the pathogenesis of Ataxia, sensory, 1, autosomal dominant, and Spastic paraplegia 85, autosomal recessive. These associations highlight its potential as a target for therapeutic intervention in neurodegenerative disorders characterized by ataxia and spastic paraplegia.

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