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.


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.


 

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
P60520

UPID:
GBRL2_HUMAN

ALTERNATIVE NAMES:
GABA(A) receptor-associated protein-like 2; Ganglioside expression factor 2; General protein transport factor p16; Golgi-associated ATPase enhancer of 16 kDa; MAP1 light chain 3-related protein

ALTERNATIVE UPACC:
P60520; O08765; Q6FG91; Q9DCP8; Q9UQF7

BACKGROUND:
The protein Gamma-aminobutyric acid receptor-associated protein-like 2, also known as GABARAPL2, is integral to the regulation of intra-Golgi traffic and autophagy. It enhances NSF ATPase activity, facilitating SNAREs activation for intra-Golgi transport. GABARAPL2's role extends to autophagy and mitophagy, regulating mitochondrial quantity and quality, thereby controlling cellular energy requirements and ROS levels. It is crucial for the maturation of autophagosomes, underscoring its importance in cellular mechanisms.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Gamma-aminobutyric acid receptor-associated protein-like 2 offers a pathway to novel therapeutic avenues. Given its essential roles in autophagy and mitophagy, key processes for cellular maintenance and energy homeostasis, targeting GABARAPL2 could lead to breakthroughs in treating conditions associated with cellular dysfunction.

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