Focused On-demand Library for Serine protease HTRA3

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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

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:

High-temperature requirement factor A3; Pregnancy-related serine protease

Alternative UPACC:

P83110; Q7Z7A2


Serine protease HTRA3, also known as High-temperature requirement factor A3 and Pregnancy-related serine protease, plays a pivotal role in various biological processes. It is known for its ability to cleave beta-casein/CSN2 and several extracellular matrix (ECM) proteoglycans including decorin/DCN, biglycan/BGN, and fibronectin/FN1. This protease is instrumental in inhibiting TGF-beta family proteins signaling, potentially through the degradation of ECM proteoglycans. HTRA3's functions extend to acting as a tumor suppressor, regulating trophoblast invasion during placental development, and contributing to ovarian development and granulosa cell differentiation.

Therapeutic significance:

Understanding the role of Serine protease HTRA3 could open doors to potential therapeutic strategies. Its involvement in crucial biological processes and potential tumor-suppressing capabilities highlight its significance in drug discovery and development.

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