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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We employ our advanced, specialised process to create targeted 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.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.


PARTNER
Receptor.AI
 
UPACC
Q8TCE9

UPID:
PPL13_HUMAN

ALTERNATIVE NAMES:
Charcot-Leyden crystal protein 2; Galectin-14

ALTERNATIVE UPACC:
Q8TCE9; A8MPV8; B2R530; C5HZ19; Q7Z4X8; Q96KD4; Q96KD5; Q96KD6; Q9NR03

BACKGROUND:
The protein known as Placental protein 13-like, with alternative names Charcot-Leyden crystal protein 2 and Galectin-14, is distinguished by its binding affinity for beta-galactoside and lactose. This property underlines its significance in the biological process of carbohydrate recognition. Moreover, its role as a strong inducer of T-cell apoptosis positions it as a key player in immune system regulation.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Placental protein 13-like offers a pathway to innovative therapeutic approaches. Given its critical role in inducing T-cell apoptosis, it holds potential for the development of novel immune-modulatory therapies.

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