AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Beta-defensin 103

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 pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.

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

P81534

UPID:

D103A_HUMAN

Alternative names:

Beta-defensin 3; Defensin, beta 103; Defensin-like protein

Alternative UPACC:

P81534; Q8NFG6; Q9NPF6

Background:

Beta-defensin 103, also known as Defensin, beta 103 and Defensin-like protein, plays a crucial role in the innate immune response. Exhibiting potent antimicrobial activity, it targets a broad spectrum of pathogens including Gram-positive bacteria (S. aureus, S. pyogenes), Gram-negative bacteria (P. aeruginosa, E. coli), and the yeast C. albicans. Notably, it is effective against multiresistant S. aureus and vancomycin-resistant E. faecium, while demonstrating minimal hemolytic activity.

Therapeutic significance:

Understanding the role of Beta-defensin 103 could open doors to potential therapeutic strategies. Its broad-spectrum antimicrobial activity positions it as a promising candidate for developing novel antimicrobial agents, especially in an era where antibiotic resistance is a growing concern.

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