Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover’s Distance

IF 4.7 Q1 POLYMER SCIENCE ACS polymers Au Pub Date : 2024-01-10 DOI:10.1021/acspolymersau.3c00029
Jiale Shi, Dylan Walsh, Weizhong Zou, Nathan J. Rebello, Michael E. Deagen, Katharina A. Fransen, Xian Gao, Bradley D. Olsen* and Debra J. Audus*, 
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Abstract

Synthetic polymers, in contrast to small molecules and deterministic biomacromolecules, are typically ensembles composed of polymer chains with varying numbers, lengths, sequences, chemistry, and topologies. While numerous approaches exist for measuring pairwise similarity among small molecules and sequence-defined biomacromolecules, accurately determining the pairwise similarity between two polymer ensembles remains challenging. This work proposes the earth mover’s distance (EMD) metric to calculate the pairwise similarity score between two polymer ensembles. EMD offers a greater resolution of chemical differences between polymer ensembles than the averaging method and provides a quantitative numeric value representing the pairwise similarity between polymer ensembles in alignment with chemical intuition. The EMD approach for assessing polymer similarity enhances the development of accurate chemical search algorithms within polymer databases and can improve machine learning techniques for polymer design, optimization, and property prediction.

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通过地球移动距离计算聚合物组合的成对相似性
与小分子和确定性生物大分子不同,合成聚合物通常是由具有不同数量、长度、序列、化学性质和拓扑结构的聚合物链组成的集合体。虽然有许多方法可以测量小分子和序列确定的生物大分子之间的成对相似性,但准确确定两个聚合物集合之间的成对相似性仍然具有挑战性。这项研究提出了地球移动者距离(EMD)度量法来计算两个聚合物组合之间的成对相似性得分。与平均法相比,EMD 能更准确地反映聚合物组之间的化学差异,并提供一个量化的数值,代表聚合物组之间的成对相似性,与化学直觉相一致。评估聚合物相似性的 EMD 方法有助于在聚合物数据库中开发精确的化学搜索算法,并能改进用于聚合物设计、优化和性能预测的机器学习技术。
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