Keywords: Polymer Porperty Prediction, Transfer Learning, Diffusion Model
TL;DR: We propose a novel transfer learning approach for the target of polymer property prediction that leverages unlabeled source molecules with the diffusion model and outperforms existing semi/self-supervised learning approaches.
Abstract: Polymers are important and numerous. While the structure synthesis and property annotation for polymers require expensive equipment and a long time of effort, small molecules without annotations have been collected from various sources and at a large scale. However, there is a lack of studies for effective transfer learning from molecules without labels (as the source domain) to polymers with labels (as the target domain). This paper proposes to extract the knowledge underlying the large set of source molecules as a specific set of useful graphs to augment the training set for target polymers. We learn a diffusion probabilistic model on the source data and design two new objectives to guide the model's denoising process with target data to generate target-specific labeled graphs. Experiments from unlabeled molecules to labeled polymers demonstrate that our transfer learning approach outperforms existing semi/self-supervised learning approaches.