Our integrated drug research platform focuses on hit identification and lead optimization to produce high-quality pre-clinical candidate molecules. Our workflow is empowered by AI computation, wet lab experimentation and research experience in medicinal chemistry. Our work scope is comprehensive and includes molecule generation, evaluation on drug-like properties and optimization, ADMET properties prediction, chemical synthesis and biological functional studies. Leveraging our platform, we aspire to revolutionize the classical methods for drug discovery and development by improving the speed, scale, novelty and diversity for pharmaceutical innovations in China and around the world.

The diagram below illustrates the overall workflow for small-molecule drug design:

Chemical Space Exploration

We deploy A.I. algorithms to explore greater chemical space and discover new chemical compound structures that address innovative targets and overcome conventional limitations in pharmaceutical innovation and intellectual property.

Drug-likeness prediction and optimization

Based on open-source and in-house data, our A.I. algorithms can deliver multi-objective optimization for key drug-like properties. This process helps identify the optimal profile for drug candidates, decrease development risk, and improves the overall success rate for drug research.

Wet lab validation and model iteration

XtalPi owns a standardized chemical synthesis platform which provides a guarantee for the functional research of drug-like compounds, and the reliable wet experimental data that effectively iterates on the calculation model, making the model more accurate, stable, and further improving the R&D efficiency.