Focused On-demand Library for Microtubule-associated proteins 1A/1B light chain 3C

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.

Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.

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

Q9BXW4

UPID:

MLP3C_HUMAN

Alternative names:

Autophagy-related protein LC3 C; Autophagy-related ubiquitin-like modifier LC3 C; MAP1 light chain 3-like protein 3; MAP1A/MAP1B light chain 3 C; Microtubule-associated protein 1 light chain 3 gamma

Alternative UPACC:

Q9BXW4; A0PJY8; A2RUP0

Background:

Microtubule-associated proteins 1A/1B light chain 3C, also known as Autophagy-related protein LC3 C, plays a pivotal role in antibacterial autophagy by selectively binding to CALCOCO2. It is instrumental in recruiting ATG8 family members to bacteria like S.typhimurium and may also contribute to aggrephagy, the degradation of ubiquitinated proteins.

Therapeutic significance:

Understanding the role of Microtubule-associated proteins 1A/1B light chain 3C could open doors to potential therapeutic strategies.