Focused On-demand Library for Lethal(3)malignant brain tumor-like protein 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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.







Alternative names:


Alternative UPACC:

Q96JM7; Q4VXE1; Q5VUM9; Q6P9B5


Lethal(3)malignant brain tumor-like protein 3 (MBT-1), encoded by the gene with the UniProt accession number Q96JM7, plays a crucial role in cellular processes. It acts as a negative regulator of Notch target genes, essential for RBPJ-mediated transcriptional repression. MBT-1 facilitates the recruitment of KDM1A to Notch-responsive elements, promoting H3K4me demethylation. Additionally, it regulates the ubiquitin-dependent degradation of methylated non-histone proteins, including SOX2, DNMT1, and E2F1, by serving as an adapter for the CRL4-DCAF5 E3 ubiquitin ligase complex. This protein is also vital for the normal maturation of myeloid progenitor cells.

Therapeutic significance:

Understanding the role of Lethal(3)malignant brain tumor-like protein 3 could open doors to potential therapeutic strategies.

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.