AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Potassium voltage-gated channel subfamily KQT member 4

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

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.

Our high-tech, dedicated method is applied to construct targeted libraries for ion channels.

 Fig. 1. The sreening workflow of Receptor.AI

This includes extensive molecular simulations of the ion channel in its native membrane environment, in open, closed, and inactivated forms, paired with ensemble virtual screening that factors in conformational mobility in each state. Tentative binding pockets are considered in the pore, the gating region, and allosteric areas to capture the full range of mechanisms of action.

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

P56696

UPID:

KCNQ4_HUMAN

Alternative names:

KQT-like 4; Potassium channel subunit alpha KvLQT4; Voltage-gated potassium channel subunit Kv7.4

Alternative UPACC:

P56696; O96025

Background:

Potassium voltage-gated channel subfamily KQT member 4 (Kv7.4) plays a crucial role in regulating neuronal excitability. It is implicated in the modulation of sensory cells in the cochlea, suggesting its importance in auditory functions. Kv7.4 channels are sensitive to various pharmacological agents, including linopirdine and XE991.

Therapeutic significance:

Kv7.4's involvement in autosomal dominant deafness, 2A highlights its potential as a therapeutic target. Understanding its role could pave the way for innovative treatments for sensorineural hearing loss, offering hope for affected individuals.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.