AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Basigin

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.

Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.

We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

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

P35613

UPID:

BASI_HUMAN

Alternative names:

5F7; Collagenase stimulatory factor; Extracellular matrix metalloproteinase inducer; Hepatoma-associated antigen; Leukocyte activation antigen M6; OK blood group antigen; Tumor cell-derived collagenase stimulatory factor

Alternative UPACC:

P35613; A6NJW1; D3YLG5; Q7Z796; Q8IZL7

Background:

Basigin, also known as BSG or CD147, is a multifunctional transmembrane protein involved in various physiological and pathological processes. It plays a crucial role in retinal development, acting as a receptor for NXNL1 to support the survival of retinal cone photoreceptors. Basigin is also pivotal in enhancing aerobic glycolysis in photoreceptors, facilitating glucose entry. Additionally, it serves as a receptor for erythrocyte invasion by P. falciparum, contributing to malaria pathogenesis. Its interaction with cyclophilins is essential for immune cell chemotaxis and adhesion.

Therapeutic significance:

Understanding the role of Basigin could open doors to potential therapeutic strategies. Its involvement in retinal health, immune response, and disease pathogenesis, including malaria and viral infections, highlights its potential as a target for therapeutic intervention. Developing inhibitors or modulators of Basigin could lead to novel treatments for a range of conditions.

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