Focused On-demand Library for Alsin

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.

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.

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.







Alternative names:

Amyotrophic lateral sclerosis 2 chromosomal region candidate gene 6 protein; Amyotrophic lateral sclerosis 2 protein

Alternative UPACC:

Q96Q42; Q53TT1; Q53TV2; Q8N1E0; Q96PC4; Q96Q41; Q9H973; Q9HCK9


Alsin, encoded by the gene implicated in Amyotrophic lateral sclerosis 2, functions as a potential GTPase regulator. It plays a crucial role in the survival and growth of spinal motoneurons. The protein is also known by names such as Amyotrophic lateral sclerosis 2 chromosomal region candidate gene 6 protein and Amyotrophic lateral sclerosis 2 protein.

Therapeutic significance:

Alsin is linked to neurodegenerative disorders including Amyotrophic lateral sclerosis 2, Juvenile primary lateral sclerosis, and Infantile-onset ascending spastic paralysis. These associations highlight its potential as a target for therapeutic intervention in these debilitating conditions.

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