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 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 carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.

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

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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