AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Probable maltase-glucoamylase 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner 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.

Our top-notch dedicated system is used to design specialised 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.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q2M2H8

UPID:

MGAL_HUMAN

Alternative names:

Maltase-glucoamylase (alpha-glucosidase) pseudogene

Alternative UPACC:

Q2M2H8; A4D2I3; C9JNC2

Background:

Probable maltase-glucoamylase 2, also known as Maltase-glucoamylase (alpha-glucosidase) pseudogene, plays a crucial role in carbohydrate metabolism by breaking down complex sugars into glucose. This enzyme's activity is essential for the efficient digestion of starch, making it a vital component in the human digestive system.

Therapeutic significance:

Understanding the role of Probable maltase-glucoamylase 2 could open doors to potential therapeutic strategies. Its pivotal function in carbohydrate digestion suggests that modulation of its activity could offer new avenues for treating metabolic disorders and enhancing nutritional absorption.

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