AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for ATP-binding cassette sub-family B member 10, mitochondrial

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

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 utilise our cutting-edge, exclusive workflow to develop 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

Q9NRK6

UPID:

ABCBA_HUMAN

Alternative names:

ABC-mitochondrial erythroid protein; ATP-binding cassette transporter 10; Mitochondrial ATP-binding cassette 2

Alternative UPACC:

Q9NRK6; Q13040; Q6P1Q8; Q9H3V0

Background:

ATP-binding cassette sub-family B member 10, mitochondrial (ABCB10), also known as ABC-mitochondrial erythroid protein, plays a crucial role in cellular processes. It catalyzes the ATP-dependent export of physiological substrates from the mitochondrial matrix to the cytosol, contributing to the heme biosynthetic process by facilitating the insertion of iron into protoporphyrin IX. This process is vital for hemoglobin synthesis and protection against oxidative stress. ABCB10's involvement in the mitochondrial unfolded protein response (UPRmt) signaling pathway highlights its significance in maintaining mitochondrial function.

Therapeutic significance:

Understanding the role of ATP-binding cassette sub-family B member 10, mitochondrial could open doors to potential therapeutic strategies. Its pivotal role in hemoglobin synthesis and mitochondrial protection suggests that targeting ABCB10 could offer novel approaches for treating conditions related to oxidative stress and mitochondrial dysfunction.

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