Focused On-demand Libraries - Receptor.AI 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.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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


 

Fig. 1. The screening 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
Receptor.AI
 
UPACC
Q96TA1

UPID:
NIBA2_HUMAN

ALTERNATIVE NAMES:
Meg-3; Melanoma invasion by ERK; Niban-like protein 1; Protein FAM129B

ALTERNATIVE UPACC:
Q96TA1; Q4LE55; Q5VVW6; Q5VVW7; Q9BUS2; Q9NT35

BACKGROUND:
The multifunctional Protein Niban 2, with aliases including Meg-3 and Melanoma invasion by ERK, is a key player in the regulation of cell death and tumor progression. Its ability to suppress apoptosis and promote invasion in melanoma cells in vitro positions it as a significant molecule in the study of cancer mechanisms.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Protein Niban 2 offers promising avenues for therapeutic intervention. Given its critical role in inhibiting apoptosis and enhancing melanoma cell invasion, targeting Protein Niban 2 could lead to innovative treatments for cancer.

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