AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Amyloid beta precursor like protein 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

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

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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q06481

UPID:

APLP2_HUMAN

Alternative names:

APPH; Amyloid beta (A4) precursor-like protein 2; Amyloid protein homolog; Amyloid-like protein 2; CDEI box-binding protein; Sperm membrane protein YWK-II

Alternative UPACC:

Q06481; B3KXX9; H7BXI4; Q13861; Q14594; Q14662; Q71U10; Q7M4L3; Q9BT36

Background:

Amyloid beta precursor like protein 2 (APPH), also known as Amyloid beta (A4) precursor-like protein 2, plays a crucial role in hemostasis regulation. It interacts with G-protein signaling pathways and binds to DNA CDEI box. APPH inhibits several proteases including trypsin and plasmin, and modulates the degradation of GPC1 heparan sulfate side chains, showcasing its multifaceted biological functions.

Therapeutic significance:

Understanding the role of Amyloid beta precursor like protein 2 could open doors to potential therapeutic strategies.

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