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.


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.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We utilise our cutting-edge, exclusive workflow to develop focused libraries for protein-protein interfaces.


 

Fig. 1. The screening workflow of Receptor.AI

It features thorough molecular simulations of the target protein, both isolated and in complex with key partner proteins, complemented by ensemble virtual screening that accounts for conformational mobility in the unbound and complex states. The tentative binding sites are explored on the protein-protein interaction interface and at remote allosteric locations, encompassing the entire spectrum of potential mechanisms of action.


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
P28799

UPID:
GRN_HUMAN

ALTERNATIVE NAMES:
Acrogranin; Epithelin precursor; Glycoprotein of 88 Kda; Granulin precursor; PC cell-derived growth factor; Proepithelin

ALTERNATIVE UPACC:
P28799; D3DX55; P23781; P23782; P23783; P23784; Q53HQ8; Q53Y88; Q540U8; Q9BWE7; Q9H8S1; Q9UCH0

BACKGROUND:
Progranulin acts as a growth factor and key regulator of lysosomal functions, involved in various cellular processes including inflammation, wound healing, and cell proliferation. It plays a role in the acidification of lysosomes and promotes epithelial cell proliferation while inhibiting their proliferation under certain conditions.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in diseases such as frontotemporal dementia and neuronal ceroid lipofuscinosis, Progranulin presents a promising target for therapeutic intervention. Exploring Progranulin's mechanisms could unlock new pathways for treating these complex diseases.

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