Focused On-demand Libraries - Receptor.AI 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 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 for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
P40394

UPID:
ADH7_HUMAN

ALTERNATIVE NAMES:
Alcohol dehydrogenase class 4 mu/sigma chain; Alcohol dehydrogenase class IV mu/sigma chain; Gastric alcohol dehydrogenase; Omega-hydroxydecanoate dehydrogenase ADH7; Retinol dehydrogenase

ALTERNATIVE UPACC:
P40394; A2RRB6; A8MVN9; B2R760; B4DWV6; Q13713

BACKGROUND:
The enzyme All-trans-retinol dehydrogenase [NAD(+)] ADH7, known for its roles in the metabolism of retinoids and fatty acids, is pivotal in the oxidation of all-trans-retinol and the reduction of all-trans-retinal. Its efficiency in catalyzing oxidative reactions underscores its importance in physiological processes such as vision and the elimination of harmful aldehydes.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of All-trans-retinol dehydrogenase [NAD(+)] ADH7 offers a promising avenue for the development of novel therapeutic approaches. Its central role in managing oxidative stress and retinoid metabolism makes it a compelling target for drug discovery efforts aimed at treating metabolic and degenerative diseases.

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