Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


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.


We utilise our cutting-edge, exclusive workflow to develop focused libraries.


 

Fig. 1. The screening 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
Receptor.AI
 
UPACC
P62314

UPID:
SMD1_HUMAN

ALTERNATIVE NAMES:
Sm-D autoantigen; snRNP core protein D1

ALTERNATIVE UPACC:
P62314; B5BTZ1; P13641

BACKGROUND:
The Small nuclear ribonucleoprotein Sm D1, recognized as Sm-D autoantigen and snRNP core protein D1, is integral to the splicing mechanism of pre-mRNA. As a fundamental element of the spliceosomal U1, U2, U4, and U5 snRNPs, it underpins the spliceosome's architecture and operational dynamics. This protein's involvement in both the pre-catalytic spliceosome B complex and activated spliceosome C complexes highlights its essential role in U12-type intron splicing. Its probable function as a charged protein scaffold for snRNP assembly or snRNP-snRNP interaction through RNA contacts underscores its importance in cellular processes.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Small nuclear ribonucleoprotein Sm D1 could open doors to potential therapeutic strategies.

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