Focused On-demand Library for Mothers against decapentaplegic homolog 7

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.

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.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

Our high-tech, dedicated method is applied to construct 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 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:

Mothers against decapentaplegic homolog 8; SMAD family member 7

Alternative UPACC:

O15105; B7Z773; K7EQ10; O14740; Q6DK23


Mothers against decapentaplegic homolog 7 (SMAD7), also known as SMAD family member 7, plays a pivotal role in cellular processes by acting as an antagonist of TGF-beta signaling. It inhibits TGF-beta and activin signaling pathways by associating with their receptors, thus blocking SMAD2 access. SMAD7 also recruits SMURF2 to the TGF-beta receptor complex and the PPP1R15A-PP1 complex to TGFBR1, enhancing dephosphorylation and positively regulating PDPK1 kinase activity.

Therapeutic significance:

Given its crucial role in modulating TGF-beta signaling, SMAD7 is directly linked to the pathogenesis of Colorectal cancer 3. Its involvement suggests that targeting SMAD7 could offer a novel therapeutic approach for managing colorectal cancer, particularly in cases where genetic susceptibility is influenced by variants affecting this gene.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.