Focused On-demand Library for Putative pre-mRNA-splicing factor ATP-dependent RNA helicase DHX32

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.

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.

We employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.







Alternative names:

DEAD/H box 32; DEAD/H helicase-like protein 1; DEAH box protein 32; HuDDX32

Alternative UPACC:

Q7L7V1; A8MSV2; D3DRF9; Q49AG5; Q5T3L0; Q5T3L5; Q96NY1; Q9BUN0; Q9H769; Q9NSL5; Q9NV74; Q9NVJ7


The Putative pre-mRNA-splicing factor ATP-dependent RNA helicase DHX32, known by alternative names such as DEAD/H box 32 and DEAH box protein 32, plays a crucial role in RNA processing. Its involvement in the intricate process of pre-mRNA splicing underscores its importance in cellular function and gene expression regulation.

Therapeutic significance:

Understanding the role of Putative pre-mRNA-splicing factor ATP-dependent RNA helicase DHX32 could open doors to potential therapeutic strategies. Its pivotal role in RNA processing makes it a promising target for drug discovery, aiming to modulate gene expression in disease conditions.

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