Focused On-demand Library for Mothers against decapentaplegic homolog 3

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 top-notch dedicated system is used to design specialised 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:

JV15-2; SMAD family member 3

Alternative UPACC:

P84022; A8K4B6; B7Z4Z5; B7Z6M9; B7Z9Q2; F5H383; O09064; O09144; O14510; O35273; Q92940; Q93002; Q9GKR4


Mothers against decapentaplegic homolog 3 (SMAD3), also known as JV15-2, plays a pivotal role in the TGF-beta signaling pathway. This pathway is crucial for various cellular processes, including proliferation, differentiation, and apoptosis. SMAD3 acts as a signal transducer and transcriptional modulator, influencing the expression of numerous genes regulated by TGF-beta.

Therapeutic significance:

SMAD3's involvement in diseases such as Colorectal cancer and Loeys-Dietz syndrome 3 highlights its potential as a therapeutic target. Its role in the progression of colorectal cancer and its association with arterial aneurysms in Loeys-Dietz syndrome 3 suggest that modulating SMAD3 activity could offer new avenues for treatment strategies.

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