AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Kelch-like protein 12

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.

Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q53G59

UPID:

KLH12_HUMAN

Alternative names:

CUL3-interacting protein 1; DKIR homolog

Alternative UPACC:

Q53G59; A6NEN8; B7Z7B8; Q9HBX5

Background:

Kelch-like protein 12 (KLHL12), also known as CUL3-interacting protein 1 or DKIR homolog, plays a pivotal role in cellular processes. It serves as a substrate-specific adapter of a BCR (BTB-CUL3-RBX1) E3 ubiquitin ligase complex, crucial for negative regulation of the Wnt signaling pathway and ER-Golgi transport. The BCR(KLHL12) complex is instrumental in ER-Golgi transport by regulating COPII coats size, essential for collagen export and embryonic stem cells division. It mediates monoubiquitination of SEC31, facilitating neural crest specification and collagen export. Additionally, it negatively regulates the Wnt signaling pathway through ubiquitination and proteolysis of DVL3.

Therapeutic significance:

Understanding the role of Kelch-like protein 12 could open doors to potential therapeutic strategies.

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