The ML workflow consists of two different steps of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of prediction models that describe various polymeric properties (e.g., thermal conductivity, glass transition temperature) as a function of chemical structures in the constitutional repeat units. Here, an ML framework called transfer learning was used to overcome the issue of limited data on thermal conductivity: prediction models of some proxy properties were pre-trained on given large data sets, and then the pre-trained models were fine-tuned using the limited data on the target property. Inverting the trained forward models, we derived a backward model conditioned by a desired property requirement. By solving this inverse problem, materials that exhibit the desired properties were computationally be created.