AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for AP-3 complex subunit beta-1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner 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 sreening 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 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

O00203

UPID:

AP3B1_HUMAN

Alternative names:

Adaptor protein complex AP-3 subunit beta-1; Adaptor-related protein complex 3 subunit beta-1; Beta-3A-adaptin; Clathrin assembly protein complex 3 beta-1 large chain

Alternative UPACC:

O00203; E5RJ68; O00580; Q7Z393; Q9HD66

Background:

The AP-3 complex subunit beta-1, also known as Beta-3A-adaptin, plays a crucial role in protein sorting within the late-Golgi/trans-Golgi network and endosomes. It is part of the adaptor protein complex 3 (AP-3), essential for the recruitment of clathrin to membranes and the recognition of sorting signals in transmembrane cargo molecules. AP-3 is specifically involved in directing a subset of transmembrane proteins to lysosomes and lysosome-related organelles, working alongside the BLOC-1 complex.

Therapeutic significance:

AP-3 complex subunit beta-1's involvement in Hermansky-Pudlak syndrome 2, characterized by oculocutaneous albinism, bleeding disorders, and immunodeficiency, highlights its therapeutic significance. Understanding its role could lead to novel therapeutic strategies for managing this syndrome and related lysosomal storage disorders.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.