Focused On-demand Library for Disks large homolog 3

Focused On-demand Libraries - Receptor.AI 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.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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.


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


 

Fig. 1. The screening 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 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
Receptor.AI
 
UPACC
Q92796

UPID:
DLG3_HUMAN

ALTERNATIVE NAMES:
Neuroendocrine-DLG; Synapse-associated protein 102; XLMR

ALTERNATIVE UPACC:
Q92796; B4E0H1; D3DVU5; Q5JUW6; Q5JUW7; Q9ULI8

BACKGROUND:
The protein Disks large homolog 3, with alternative names Neuroendocrine-DLG, Synapse-associated protein 102, and XLMR, is required for synaptic plasticity, which is fundamental for learning processes. Its involvement in NMDA receptor signaling underscores its importance in cognitive functions.

THERAPEUTIC SIGNIFICANCE:
Linked to Intellectual developmental disorder, X-linked 90, characterized by intellectual deficiency and adaptive behavior impairments, Disks large homolog 3 presents a target for therapeutic intervention. Exploring its functions could lead to novel treatments for intellectual disabilities.

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