Focused On-demand Library for AP-3 complex subunit beta-2

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.

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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

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.







Alternative names:

Adaptor protein complex AP-3 subunit beta-2; Adaptor-related protein complex 3 subunit beta-2; Beta-3B-adaptin; Clathrin assembly protein complex 3 beta-2 large chain; Neuron-specific vesicle coat protein beta-NAP

Alternative UPACC:

Q13367; A4Z4T7; B7ZKR7; B7ZKS0; O14808; Q52LY8


The AP-3 complex subunit beta-2, known by various names such as Beta-3B-adaptin and Neuron-specific vesicle coat protein beta-NAP, plays a crucial role in protein sorting within the late-Golgi/trans-Golgi network and endosomes. It is a part of the adaptor protein complex 3 (AP-3) which is essential for the sorting of transmembrane proteins targeted to lysosomes and related organelles, facilitating their delivery into neurites and nerve terminals.

Therapeutic significance:

Given its involvement in Developmental and epileptic encephalopathy 48, a severe early-onset epilepsy with neurodevelopmental impairment, understanding the role of AP-3 complex subunit beta-2 could open doors to potential therapeutic strategies aimed at mitigating this debilitating condition.

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