AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for LIM and senescent cell antigen-like-containing domain protein 2

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.

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 employ our advanced, specialised process to create targeted 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.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.

partner

Reaxense

upacc

Q7Z4I7

UPID:

LIMS2_HUMAN

Alternative names:

LIM-like protein 2; Particularly interesting new Cys-His protein 2

Alternative UPACC:

Q7Z4I7; A6NLH0; B4DMV1; F5H6E6; Q7Z4I2; Q7Z4I6; Q7Z4I8; Q8NFE7; Q9HA13

Background:

LIM and senescent cell antigen-like-containing domain protein 2, also known as LIM-like protein 2 and Particularly interesting new Cys-His protein 2, plays a pivotal role in connecting beta-integrins to the actin cytoskeleton. This adapter protein facilitates cell spreading and migration by linking the cytoplasmic complex to cell surface receptor tyrosine kinases and growth factor receptors.

Therapeutic significance:

The protein is implicated in muscular dystrophy, autosomal recessive, with cardiomyopathy and triangular tongue, a condition characterized by early-onset muscle weakness, severe quadriparesis, biventricular cardiac dysfunction, and a distinctive triangular tongue. Understanding the role of LIM and senescent cell antigen-like-containing domain protein 2 could open doors to potential therapeutic strategies for this debilitating disease.

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