Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


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 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
Q9BXW4

UPID:
MLP3C_HUMAN

ALTERNATIVE NAMES:
Autophagy-related protein LC3 C; Autophagy-related ubiquitin-like modifier LC3 C; MAP1 light chain 3-like protein 3; MAP1A/MAP1B light chain 3 C; Microtubule-associated protein 1 light chain 3 gamma

ALTERNATIVE UPACC:
Q9BXW4; A0PJY8; A2RUP0

BACKGROUND:
The protein Microtubule-associated proteins 1A/1B light chain 3C, known for its roles in xenophagy through CALCOCO2 binding and aggrephagy, is crucial in the body's defense against bacterial infections and in the clearance of aggregated proteins.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Microtubule-associated proteins 1A/1B light chain 3C offers promising avenues for the development of novel therapeutic approaches.

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