Evidence map›Paper›PMID 38371731›Full record

ArticleACS polymers Au2024

Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.

Jiale Shi, Dylan Walsh, Weizhong Zou, Nathan J Rebello, Michael E Deagen, Katharina A Fransen, Xian Gao, Bradley D Olsen, Debra J Audus

Abstract read
In one paragraph

Article in ACS polymers Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jiale ShiDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0002-5447-3925
Dylan WalshDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0001-7981-2770
Weizhong ZouDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Nathan J RebelloDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0002-0178-7701
Michael E DeagenDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0002-8034-0667
Katharina A FransenDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Xian GaoDepartment of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States.
Bradley D OlsenDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0002-7272-7140
Debra J AudusMaterials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.ORCID https://orcid.org/0000-0002-5937-7721

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID38371731
PMCPMC10870752

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.