AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Toll-like receptor 4

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.

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.

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

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

partner

Reaxense

upacc

O00206

UPID:

TLR4_HUMAN

Alternative names:

hToll

Alternative UPACC:

O00206; A8K1Y8; A9XLP9; A9XLQ0; A9XLQ1; B4E194; D1CS52; D1CS53; Q5VZI8; Q5VZI9; Q9UK78; Q9UM57

Background:

Toll-like receptor 4 (TLR4), also known as hToll, is a pivotal transmembrane receptor in the innate immune system. It recognizes pathogen- and damage-associated molecular patterns to trigger immune responses. TLR4's activation involves signaling pathways leading to NF-kappa-B activation and cytokine secretion, crucial for the inflammatory response. It also plays a role in LPS-independent responses and influences the NLRP3 inflammasome and autophagy.

Therapeutic significance:

Understanding the role of Toll-like receptor 4 could open doors to potential therapeutic strategies. Its involvement in innate immunity and inflammatory processes makes it a target for developing treatments for inflammatory diseases.

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