AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Interleukin-3

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.

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.

Our top-notch dedicated system is used to design specialised 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 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

P08700

UPID:

IL3_HUMAN

Alternative names:

Hematopoietic growth factor; Mast cell growth factor; Multipotential colony-stimulating factor; P-cell-stimulating factor

Alternative UPACC:

P08700; Q6GS87

Background:

Interleukin-3, known as a Hematopoietic growth factor, plays a pivotal role in the regulation of hematopoietic progenitor cells differentiation and lineage restriction. It is secreted by T-lymphocytes, mast cells, and osteoblastic cells, influencing various cell types including basophils, eosinophils, and monocytes. Beyond hematopoiesis, Interleukin-3 is crucial for neural cell proliferation and bone homeostasis, inhibiting osteoclast differentiation through NF-kappa-B pathway modulation.

Therapeutic significance:

Understanding the role of Interleukin-3 could open doors to potential therapeutic strategies. Its involvement in cell proliferation, survival, and differentiation highlights its potential as a target for treating hematopoietic disorders and bone diseases.

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