Abstract
The design of novel functional polymer resin with experiments costs time and money. Thus, the design with chemoinformatics is desired; ingredients for novel polymers are, however, in general hardly found in the market, which results in a small-sized dataset. Linear model with polymer descriptors obtained as linear combination of monomer descriptors and compositions was used in this study so as to model an industrial small-sized dataset. The monomers were expressed with Morgan fingerprint with radii 0-3, and PLS model was employed. The main feature of the model is to be able to estimate the contribution of each monomer to the objective variable, when setting a mole fraction of a monomer one. After the model construction, optimization of monomers’ compositions and structure generation were carried out. A novel combination of monomers obtained in the optimization step made the objective variable the most desired one. The optimization results were verified by experiments. In the structure generation step, the regression model with the fragment descriptors was incorporated mol2vec, a sort of auto-encoder in the deep learning field, into. With both the linear model and mol2vec, structure generation provided a large number of structures that were possibly effective for polymer resin with the desired objective variable.
