Quantifying Pairwise Chemical Similarity of Polymers

Computing the similarity between polymers is an important task in determining the diversity of different polymer libraries, developing search methods for polymer databases, and developing AI algorithms that can explore chemical space. In contrast to small molecules which have well-defined molecule structures and existing methods for similarity calculations, chemical similarity remains an outstanding problem due to the stochastic nature of polymers. Here, we design a similarity function built upon automata as a polymer graph representation. The graph representation is decomposed into three parts: repeat units, end groups, and topology. Similarity scores for each of the three parts are computed and linearly combined to yield a pairwise chemical similarity for polymers that are tunable based on the parameters used in the linear combination to reflect the needs of the user. We demonstrate the power of our method through a variety of case studies. This method gives a promising solution to quantitatively calculate the pairwise chemical similarity for polymers and presents an essential step towards making polymer data more structured and organized.