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 stands out due to several important features:

• The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.


• Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.


• Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.


• Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.

PARTNER
Receptor.AI

 

UPACC
Q96BI1

UPID:
S22AI_HUMAN

ALTERNATIVE NAMES:
Beckwith-Wiedemann syndrome chromosomal region 1 candidate gene A protein; Efflux transporter-like protein; Imprinted multi-membrane-spanning polyspecific transporter-related protein 1; Organic cation transporter-like protein 2; Solute carrier family 22 member 1-like; Tumor-suppressing STF cDNA 5 protein; Tumor-suppressing subchromosomal transferable fragment candidate gene 5 protein; p45-Beckwith-Wiedemann region 1 A