AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Small ubiquitin-related modifier 3

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.

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.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.

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.

partner

Reaxense

upacc

P55854

UPID:

SUMO3_HUMAN

Alternative names:

SMT3 homolog 1; SUMO-2; Ubiquitin-like protein SMT3A

Alternative UPACC:

P55854; B2R5X4; B4DUW4; Q53HI9; Q6FGD4; Q9BWR4

Background:

Small ubiquitin-related modifier 3 (SUMO-2), also known as SMT3 homolog 1 and ubiquitin-like protein SMT3A, plays a pivotal role in various cellular processes. This includes nuclear transport, DNA replication and repair, mitosis, and signal transduction. SUMO-2 functions by covalently attaching to target lysines on substrates, a process facilitated by the E1 complex SAE1-SAE2, the E2 enzyme UBE2I, and potentially promoted by E3 ligases such as PIAS1-4, RANBP2, or CBX4.

Therapeutic significance:

Understanding the role of Small ubiquitin-related modifier 3 could open doors to potential therapeutic strategies.

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