Data-assisted polymer retrosynthesis planning

Abstract

Polymer informatics is being utilized to accelerate polymer discovery. However, the practical realization of the designed polymer is still slow due to synthesis challenges, e.g., difficulties with the identification of potential polymerization mechanisms and optimal reactants/solvents/processing conditions. In the past, synthesis pathways adopted for a target polymer have been heavily dependent on chemical intuition and past experience. To expedite this process, we have developed a data-driven approach to assist in polymer retrosynthesis planning. In this work, a dataset of polymerization reactions was manually accumulated from various resources to extract hundreds of synthetic templates and used as the training set. Further, a similarity metric was adopted to select synthetic templates and similar existing reactants for the new target polymer. Finally, prediction accuracy was measured by comparison with ground truth and/or bench chemists' estimation. The proposed data-driven polymer synthesis recommendation model has been deployed at https://www.polymergenome.org.