Focused On-demand Library for NLR family CARD domain-containing protein 4

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

We utilise our cutting-edge, exclusive workflow to develop focused libraries.

Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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

Q9NPP4

UPID:

NLRC4_HUMAN

Alternative names:

CARD, LRR, and NACHT-containing protein; Caspase recruitment domain-containing protein 12; Ice protease-activating factor

Alternative UPACC:

Q9NPP4; A8K9F8; B2RBQ3; B3KTF0; D6W580; Q96J81; Q96J82; Q96J83

Background:

NLR family CARD domain-containing protein 4, also known as NLRC4, plays a pivotal role in the immune system. It is a key component of inflammasomes, specialized protein complexes that detect pathogenic microorganisms and damaged cellular components to initiate an inflammatory response. NLRC4 specifically responds to proteins from pathogenic bacteria and fungi, leading to the activation of caspase-1, cytokine production, and macrophage pyroptosis.

Therapeutic significance:

NLRC4 is implicated in autoinflammatory diseases such as Autoinflammation with infantile enterocolitis and Familial cold autoinflammatory syndrome 4. These conditions are characterized by episodes of fever, inflammation, and organ-specific symptoms, caused by genetic variants affecting NLRC4. Understanding the role of NLRC4 could open doors to potential therapeutic strategies for these autoinflammatory disorders.