Targeted protein degradation (TPD) is an emerging hot spot in drug discovery that aims to develop therapeutics based on selectively degradation of disease-causing proteins. TPD recruits disease-causing proteins to the proteasomes, where they are degraded and eliminated from the cell. This novel approach enables us to innovate new medicines by targeting previously undruggable proteins for potential treatment of various human disease such as cancer, neurodegenerative disorders, autoimmune diseases and etc. 

This comprehensive platform is specifically aiming to develop new medicines with differentiated profiles, to explore previously undruggable targets, and to shorten TPD molecule development time period through efficient workflow.

◆Proteomics+ engine: This engine accelerates novel targets exploration using in-house developed proteomics and ligand discovery.

◆DaELF+ engine: This engine owns proprietary DaELF libraries (including three sub-libraries: oral ELF (E3 ligand-Linker Fragments), long acting ELF, and CNS-penetrating ELF) and uses its dimension reduction approaches to achieve different PK profiles in drug discovery. 

◆AI+ engine: This engine utilizes proprietary AI algorithms to predict binding modes, degradation ability and ADMET properties of TPD molecules.

AI is a rapidly growing field that has expanded to the pharmaceutical industry in recent years, offering significant opportunities to accelerate drug discovery and development. The pharmaceutical industry faces enormous challenges, including long development cycles, low success rates, high costs, and increasing regulatory hurdles. AI has tremendous potential to address these challenges by improving the quality of drug candidates, increasing the speed of drug development, reducing costs, and providing actionable insights for personalized medicine.