AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for StAR-related lipid transfer protein 3

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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.

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

partner

Reaxense

upacc

Q14849

UPID:

STAR3_HUMAN

Alternative names:

Metastatic lymph node gene 64 protein; Protein CAB1; START domain-containing protein 3

Alternative UPACC:

Q14849; A8MXA4; B4DUY1; F5H0G2; Q53Y53; Q96HM9

Background:

StAR-related lipid transfer protein 3, also known as Metastatic lymph node gene 64 protein, Protein CAB1, and START domain-containing protein 3, plays a pivotal role in cholesterol transport from the endoplasmic reticulum to endosomes. It functions as a sterol-binding protein, facilitating membrane formation inside endosomes and potentially mediating cholesterol transport between various membranes, including mitochondria and cell membrane. This protein's activity is enhanced by phosphorylation, promoting membrane tethering through interaction with VAPA and VAPB.

Therapeutic significance:

Understanding the role of StAR-related lipid transfer protein 3 could open doors to potential therapeutic strategies.

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