AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Protein S100-A11

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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.

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.

We employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse 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

P31949

UPID:

S10AB_HUMAN

Alternative names:

Calgizzarin; Metastatic lymph node gene 70 protein; Protein S100-C; S100 calcium-binding protein A11

Alternative UPACC:

P31949; Q5VTK0

Background:

Protein S100-A11, also known as Calgizzarin, Metastatic lymph node gene 70 protein, Protein S100-C, and S100 calcium-binding protein A11, plays a crucial role in the differentiation and cornification of keratinocytes. This protein is a member of the S100 family, characterized by their ability to bind calcium ions, which influences their function in cellular processes.

Therapeutic significance:

Understanding the role of Protein S100-A11 could open doors to potential therapeutic strategies. Its involvement in the differentiation and cornification of keratinocytes highlights its importance in skin health and disease, making it a target for therapeutic intervention in conditions affecting skin integrity and function.

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