AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Transmembrane protein 107

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.

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.

 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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q6UX40

UPID:

TM107_HUMAN

Alternative names:

-

Alternative UPACC:

Q6UX40; A0PJV7; Q6NSE3; Q6ZRX9; Q96T82

Background:

Transmembrane protein 107 (TMEM107) plays a crucial role in cilia formation and embryonic patterning. It is essential for normal Sonic hedgehog (Shh) signaling in the neural tube, collaborating with GLI2 and GLI3 to pattern ventral and intermediate neuronal cell types. TMEM107 also regulates the ciliary transition zone localization of MKS complex proteins, highlighting its significance in ciliogenesis.

Therapeutic significance:

TMEM107 is implicated in Meckel syndrome 13 and Orofaciodigital syndrome 16, both of which are characterized by developmental anomalies. Understanding the role of TMEM107 could open doors to potential therapeutic strategies for these complex disorders.

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