AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for AFG3-like protein 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We employ our advanced, specialised process to create 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.

partner

Reaxense

upacc

Q9Y4W6

UPID:

AFG32_HUMAN

Alternative names:

Paraplegin-like protein

Alternative UPACC:

Q9Y4W6; Q6P1L0

Background:

AFG3-like protein 2, also known as Paraplegin-like protein, plays a pivotal role in axonal and neuron development. It is an ATP-dependent protease essential for the degradation of specific mitochondrial proteins, facilitating neuron health and function. This protein is involved in the maturation of several key proteins within the mitochondria, including paraplegin and PINK1, and regulates mitochondrial dynamics through its interaction with OPA1 and GHITM.

Therapeutic significance:

AFG3-like protein 2 is linked to several neurodegenerative disorders, including Spinocerebellar ataxia 28, Spastic ataxia 5, and Optic atrophy 12. These associations underscore its potential as a target for therapeutic intervention in these diseases. Understanding the role of AFG3-like protein 2 could open doors to potential therapeutic strategies, offering hope for patients suffering from these debilitating conditions.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
No Spam. Cancel Anytime.