AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Fibroblast growth factor 12

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.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

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

P61328

UPID:

FGF12_HUMAN

Alternative names:

Fibroblast growth factor homologous factor 1; Myocyte-activating factor

Alternative UPACC:

P61328; B2R6B7; B2R976; O35339; P70376; Q8TBG5; Q92912; Q93001

Background:

Fibroblast growth factor 12 (FGF12), also known as Fibroblast growth factor homologous factor 1 and Myocyte-activating factor, plays a pivotal role in nervous system development and function. It is crucial in the positive regulation of voltage-gated sodium channel activity, specifically enhancing the voltage dependence of neuronal sodium channel SCN8A fast inactivation, thereby promoting neuronal excitability.

Therapeutic significance:

FGF12's involvement in Developmental and epileptic encephalopathy 47, a severe early-onset epilepsy with neurodevelopmental impairment, underscores its potential as a therapeutic target. Understanding the role of FGF12 could open doors to potential therapeutic strategies for managing this challenging condition.

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.
No Spam. Cancel Anytime.