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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


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 employ our advanced, specialised process to create targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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
Q06481

UPID:
APLP2_HUMAN

ALTERNATIVE NAMES:
APPH; Amyloid beta (A4) precursor-like protein 2; Amyloid protein homolog; Amyloid-like protein 2; CDEI box-binding protein; Sperm membrane protein YWK-II

ALTERNATIVE UPACC:
Q06481; B3KXX9; H7BXI4; Q13861; Q14594; Q14662; Q71U10; Q7M4L3; Q9BT36

BACKGROUND:
The protein Amyloid beta precursor like protein 2, with alternative names such as Amyloid protein homolog and CDEI box-binding protein, is implicated in the regulation of blood coagulation. Its soluble form possesses inhibitory properties against coagulation factors, and it plays a role in cellular signaling and DNA interaction. This protein's ability to inhibit key enzymes in the coagulation cascade highlights its significance in hemostasis.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Amyloid beta precursor like protein 2 offers a promising avenue for the development of novel therapeutic approaches.

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