Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 use our state-of-the-art dedicated workflow for designing focused 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 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
O15520

UPID:
FGF10_HUMAN

ALTERNATIVE NAMES:
Keratinocyte growth factor 2

ALTERNATIVE UPACC:
O15520; C7FDY0; Q6FHR3; Q6FHT6; Q96P59

BACKGROUND:
The protein Fibroblast growth factor 10, with an alternative name Keratinocyte growth factor 2, plays a crucial role in regulating embryonic development, cell proliferation, and differentiation. Its requirement for normal branching morphogenesis indicates its significance in the developmental processes of various bodily systems. Additionally, FGF10's contribution to wound healing processes points to its vital role in tissue repair and regeneration.

THERAPEUTIC SIGNIFICANCE:
Given its association with diseases such as Aplasia of lacrimal and salivary glands and Lacrimo-auriculo-dento-digital syndrome 3, Fibroblast growth factor 10 represents a promising target for therapeutic intervention. The exploration of FGF10's functions and mechanisms could lead to novel treatments for these rare conditions, showcasing the importance of continued research in this area.

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