Focused On-demand Library for Lethal(3)malignant brain tumor-like protein 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of 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.







Alternative names:

L(3)mbt protein homolog; L3MBTL1

Alternative UPACC:

Q9Y468; B4DRC9; E1P5W7; Q5H8Y8; Q5H8Y9; Q8IUV7; Q9H1E6; Q9H1G5; Q9UG06; Q9UJB9; Q9Y4C9


Lethal(3)malignant brain tumor-like protein 1 (L3MBTL1), also known as L(3)mbt protein homolog, plays a pivotal role in chromatin compaction and transcription repression. It functions as a 'reader' of post-translational modifications, binding to mono- and dimethyllysine residues on histones and other proteins, such as p53/TP53 and RB1/RB. This activity contributes to maintaining genes in a transcriptionally repressive state, crucial for normal cell proliferation and mitosis.

Therapeutic significance:

Understanding the role of Lethal(3)malignant brain tumor-like protein 1 could open doors to potential therapeutic strategies.

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