Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated 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 use our state-of-the-art dedicated workflow for designing focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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
Q13286

UPID:
CLN3_HUMAN

ALTERNATIVE NAMES:
Batten disease protein; Protein CLN3

ALTERNATIVE UPACC:
Q13286; B2R7J1; B4DXL3; O00668; O95089; Q549S9; Q9UP09; Q9UP11; Q9UP12; Q9UP13; Q9UP14

BACKGROUND:
The protein CLN3, known as Battenin, mediates essential cellular processes by facilitating the anterograde transport of proteins and lipids. It plays a significant role in lysosome protein degradation, receptor-mediated endocytosis, and synaptic transmission. Battenin's ability to regulate the cellular environment under stress conditions and its involvement in synaptic plasticity and memory formation make it a key player in cellular homeostasis.

THERAPEUTIC SIGNIFICANCE:
Given its critical role in neuronal ceroid lipofuscinosis type 3, a progressive neurodegenerative disease, Battenin represents a promising target for therapeutic intervention. Exploring the mechanisms by which Battenin functions could unveil new pathways for treatment, offering hope for patients affected by this debilitating condition.

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