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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide 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
Q96BT7

UPID:
ALKB8_HUMAN

ALTERNATIVE NAMES:
Probable alpha-ketoglutarate-dependent dioxygenase ABH8; S-adenosyl-L-methionine-dependent tRNA methyltransferase ABH8; tRNA (carboxymethyluridine(34)-5-O)-methyltransferase ABH8

ALTERNATIVE UPACC:
Q96BT7; B1Q2M0; B4DEF6; Q8N989

BACKGROUND:
The protein Alkylated DNA repair protein alkB homolog 8, with alternative names such as S-adenosyl-L-methionine-dependent tRNA methyltransferase ABH8, is pivotal in tRNA modification. It ensures the proper functioning of the methylation process of 5-carboxymethyl uridine, crucial for the accuracy of protein synthesis and effective DNA damage repair. ABH8's specificity towards tRNA(Arg) and tRNA(Glu) highlights its selective role in cellular mechanisms.

THERAPEUTIC SIGNIFICANCE:
Given ABH8's critical function in Intellectual developmental disorder, autosomal recessive 71, targeting this protein could offer innovative therapeutic avenues. The exploration of ABH8's mechanisms offers promising potential for developing treatments for related intellectual and developmental disorders.

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