Focused On-demand Library for Ral GTPase-activating protein subunit alpha-1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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.

Our high-tech, dedicated method is applied to construct targeted libraries.

 Fig. 1. The sreening 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 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.







Alternative names:

GAP-related-interacting partner to E12; GTPase-activating Rap/Ran-GAP domain-like 1; Tuberin-like protein 1; p240

Alternative UPACC:

Q6GYQ0; A6NMA4; B9EK38; C5NU19; O94960; Q6GYP9; Q6ZT23; Q86YF3; Q86YF5; Q8ND69


Ral GTPase-activating protein subunit alpha-1, also known as GAP-related-interacting partner to E12, GTPase-activating Rap/Ran-GAP domain-like 1, Tuberin-like protein 1, and p240, plays a pivotal role in cellular processes. It is the catalytic subunit of the RalGAP1 complex, activating the GTPases RALA and RALB, which are crucial for various cellular functions.

Therapeutic significance:

The protein is linked to a neurodevelopmental disorder characterized by severe neurodevelopmental disability, muscular hypotonia, and other symptoms. Understanding the role of Ral GTPase-activating protein subunit alpha-1 could open doors to potential therapeutic strategies for this disorder.

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