Focused On-demand Library for Kinesin-like protein KIF7

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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 high-tech, dedicated method is applied to construct targeted 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.

Key features that set our library apart include:

The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q2M1P5

UPID:

KIF7_HUMAN

Alternative names:

Alternative UPACC:

Q2M1P5; Q3SXY0; Q6UXE9; Q8IW72

Background:

Kinesin-like protein KIF7 plays a pivotal role in hedgehog signaling, a pathway crucial for cell differentiation, tissue patterning, and organogenesis. This protein acts as both a positive and negative regulator of sonic hedgehog (Shh) and Indian hedgehog (Ihh) pathways, influencing microtubular dynamics and ciliary localization of key signaling complexes. Its involvement extends to the regulation of epidermal differentiation and chondrocyte development, showcasing its multifaceted role in cellular processes.

Therapeutic significance:

KIF7 mutations are linked to a spectrum of genetic disorders, including Bardet-Biedl syndrome, Hydrolethalus syndrome 2, Acrocallosal syndrome, Joubert syndrome 12, and Al-Gazali-Bakalinova syndrome. These conditions manifest through a variety of symptoms, from developmental delays to congenital malformations, highlighting the protein's clinical relevance. Understanding KIF7's role could pave the way for innovative treatments targeting these complex syndromes.