(192j) Generating Molecules with Optimized Solubility Using Iterative Graph Translation
AIChE Annual Meeting
2021 Annual Meeting
Engineering Sciences and Fundamentals
Faculty Candidates in COMSEF/Area 1a, Session 2
Monday, November 8, 2021 - 5:31pm to 5:43pm
In this talk, we will present our work applying this framework to the specific problem of designing molecules to optimize aqueous solubility. Aqueous solubility is an important property for many chemical applications ranging from drug design to climate prediction. We successfully trained a translator to improve aqueous solubility and found that the model was also capable of discovering molecules that were more soluble than any of the training examples. When we applied synthetic feasibility as a secondary optimization constraint, the resulting model generated synthetically feasible molecules 93.2% of the time. Additionally, we investigated the role that training dataset size plays in model performance and found that reasonable models could be trained on datasets containing only 102-103 molecules. This workflow serves as a general approach for generating molecules that are both optimized and synthetically feasible. These promising results have led us to explore how this framework can be applied to solve a variety of molecular design problems including designing novel dyes, enantioselective catalysts, and soluble drugs.