AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for ELAV-like protein 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 our partner 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 sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across 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

Q15717

UPID:

ELAV1_HUMAN

Alternative names:

Hu-antigen R

Alternative UPACC:

Q15717; B4DVB8; Q53XN6; Q9BTT1

Background:

ELAV-like protein 1, also known as Hu-antigen R, plays a crucial role in RNA-binding, specifically targeting the 3'-UTR region of mRNAs to enhance their stability. This protein is pivotal in embryonic stem cell differentiation, binding to mRNAs not methylated by N6-methyladenosine (m6A), thus stabilizing them. It also interacts with m6A-containing mRNAs, including those of MYC, contributing to MYC stability. ELAV-like protein 1 binds to poly-U elements and AU-rich elements in the 3'-UTR of target mRNAs, significantly influencing the expression of genes like FOS and IL3.

Therapeutic significance:

Understanding the role of ELAV-like protein 1 could open doors to potential therapeutic strategies.

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