AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for AP-1 complex subunit gamma-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.

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 our associate Reaxense.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

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

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

O43747

UPID:

AP1G1_HUMAN

Alternative names:

Adaptor protein complex AP-1 subunit gamma-1; Adaptor-related protein complex 1 subunit gamma-1; Clathrin assembly protein complex 1 gamma-1 large chain; Gamma1-adaptin; Golgi adaptor HA1/AP1 adaptin subunit gamma-1

Alternative UPACC:

O43747; O75709; O75842; Q9UG09; Q9Y3U4

Background:

AP-1 complex subunit gamma-1, known by alternative names such as Adaptor protein complex AP-1 subunit gamma-1, plays a crucial role in protein sorting within the late-Golgi/trans-Golgi network and endosomes. It is part of the clathrin-associated adaptor protein complex 1, essential for the recruitment of clathrin to membranes and the recognition of sorting signals in transmembrane cargo molecules. This protein is also involved in the trafficking of transferrin, furin, and cathepsin D, highlighting its significance in cellular transport mechanisms.

Therapeutic significance:

AP-1 complex subunit gamma-1 is implicated in Usmani-Riazuddin syndrome, both autosomal dominant and recessive forms, characterized by developmental delays, speech impairment, and behavioral abnormalities. Understanding the role of AP-1 complex subunit gamma-1 could open doors to potential therapeutic strategies for these neurodevelopmental disorders.

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