Focused On-demand Library for V-set domain-containing T-cell activation inhibitor 1

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We employ our advanced, specialised process to create targeted libraries for protein-protein interfaces.

Fig. 1. The sreening workflow of Receptor.AI

It features thorough molecular simulations of the target protein, both isolated and in complex with key partner proteins, complemented by ensemble virtual screening that accounts for conformational mobility in the unbound and complex states. The tentative binding sites are explored on the protein-protein interaction interface and at remote allosteric locations, encompassing the entire spectrum of potential mechanisms of action.

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

Q7Z7D3

UPID:

VTCN1_HUMAN

Alternative names:

B7 homolog 4; B7h.5; Immune costimulatory protein B7-H4; Protein B7S1; T-cell costimulatory molecule B7x

Alternative UPACC:

Q7Z7D3; Q0GN76; Q45VN0; Q5WPZ3; Q6P097; Q9H6B2

Background:

V-set domain-containing T-cell activation inhibitor 1, known by alternative names such as B7 homolog 4 and Immune costimulatory protein B7-H4, plays a crucial role in the immune system. It negatively regulates T-cell-mediated immune responses by inhibiting T-cell activation, proliferation, cytokine production, and development of cytotoxicity. Additionally, it is involved in the suppression of tumor-associated antigen-specific T-cell immunity when expressed on the surface of tumor macrophages.

Therapeutic significance:

Understanding the role of V-set domain-containing T-cell activation inhibitor 1 could open doors to potential therapeutic strategies. Its involvement in T-cell regulation and tumor immunity suppression highlights its potential as a target for immunotherapy and cancer treatment.