AI-Enabled Catalyst Discovery

Thrust 2

AlphaSynthesis can be used for both manufacturing of target molecules with known functions (reverse synthesis) and discovery of molecules with new functions (forward synthesis). In both cases, identifying catalysts is critical to the success of implementing the synthetic routes. Therefore, the main goal of this Thrust is to develop new AI algorithms or approaches to discover and optimize catalysts that are required to implement the synthetic routes designed by AlphaSynthesis. In addition, these tools are being applied toward the realization of the four experimental endeavors and the catalysts required for the manufacturing of target molecules and materials in other Thrusts.


The Lead Researchers: 


The Current Projects:

  • Development of Deep Learning Models for Molecular Modeling - Improve deep learning algorithms for molecular modeling 
  • AI-Guided Development of Csp3-Csp2 Suzuki-Miyaura Cross Couplings with Unactivated Olefins - Use coevolution approach to optimize the reaction of secondary Csp3 organoboron nucleophiles with unactivated vinyl halides 
  • AI-Enabled Catalyst Discovery and Optimization - Develop state-of-the-art methods for AI-guided catalyst optimization campaign  
  • AI-Guided Development of a Selectivity Model for C-H Oxidation Catalysts - Develop a model that encompasses both iron and manganese version of White's C-H oxidation catalysta
  • Development of a Fully Closed BDTL Cycle for Enzyme Engineering - Develop new algorithms for predicting enzyme specificity and selectivity