AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Platelet-activating factor acetylhydrolase IB subunit alpha1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.

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 for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q15102

UPID:

PA1B3_HUMAN

Alternative names:

PAF acetylhydrolase 29 kDa subunit; PAF-AH subunit gamma

Alternative UPACC:

Q15102; Q53X88

Background:

The Platelet-activating factor acetylhydrolase IB subunit alpha1, also known as PAF acetylhydrolase 29 kDa subunit or PAF-AH subunit gamma, plays a pivotal role in modulating the action of platelet-activating factor (PAF) through the hydrolysis of the acetyl group at the sn-2 position of PAF and its analogs. This process is crucial for the regulation of inflammatory responses and is facilitated by the enzyme's heterotetrameric structure, which includes both alpha1/alpha1 homodimers and alpha1/alpha2 heterodimers, enhancing its substrate specificity and activity.

Therapeutic significance:

Understanding the role of Platelet-activating factor acetylhydrolase IB subunit alpha1 could open doors to potential therapeutic strategies, particularly in the modulation of inflammatory responses and the development of treatments for conditions where PAF's action is detrimental.

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