Focused On-demand Libraries - Receptor.AI 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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


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


 

Fig. 1. The screening 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 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
Receptor.AI
 
UPACC
Q02078

UPID:
MEF2A_HUMAN

ALTERNATIVE NAMES:
Serum response factor-like protein 1

ALTERNATIVE UPACC:
Q02078; B4DFQ7; F6XG23; O43814; Q14223; Q14224; Q59GX4; Q7Z6C9; Q96D14

BACKGROUND:
The protein Myocyte-specific enhancer factor 2A, alternatively named Serum response factor-like protein 1, is a transcriptional activator crucial for muscle development and growth factor-related transcription. It engages in cellular processes such as cell growth, survival, and apoptosis, and is essential for neuronal differentiation and synaptic differentiation, mediated by its interaction with the MEF2 element and p38 MAPK signaling.

THERAPEUTIC SIGNIFICANCE:
Given MEF2A's critical role in coronary artery disease and its broad involvement in cellular functions, targeting this protein could lead to innovative treatments for cardiovascular conditions and neurodegenerative diseases. Understanding MEF2A's functions offers a promising avenue for drug discovery.

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