Notation system allows scientists to communicate polymers more easily | 8/9/2019 | Staff
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Having a compact, yet robust, structurally-based identifier or representation system for molecular structures is a key enabling factor for efficient sharing and dissemination of results within the research community. Such systems also lay down the essential foundations for machine learning and other data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system.

For small molecules, the basic premise is that each distinct chemical species corresponds to a well-defined chemical structure. This does not hold for polymers. Polymers are intrinsically stochastic molecules that are often ensembles with a distribution of chemical structures. This difficulty limits the applicability of all deterministic representations developed for small molecules. In a paper published Sept. 12 in ACS Central Science, researchers at MIT, Duke University, and Northwestern University report a new representation system that is capable of handling the stochastic nature of polymers, called BigSMILES.

BigSMILES - Challenge - Representation - Polymers - Connor

"BigSMILES addresses a significant challenge in the digital representation of polymers," explains Connor Coley Ph.D. '19, co-author of the paper. "Polymers are almost always ensembles of multiple chemical structures, generated through stochastic processes, so we can't use the same strategies for writing down their structures as for small molecules."

Co-authors are Coley; associate professor of chemical engineering Bradley D. Olsen at MIT; Warren K. Lewis Professor of Chemical Engineering Klavs F. Jensen at MIT; assistant professor of chemistry Julia A. Kalow at Northwestern University; associate professor of chemistry Jeremiah A. Johnson at MIT; William T. Miller Professor of Chemistry Stephen L. Craig at Duke University; graduate student Eliot Woods at Northwestern University; graduate student Zi Wang at Duke University; graduate student Wencong Wang at MIT; graduate student Haley K. Beech at MIT; visiting researcher Hidenobu Mochigase at MIT; and graduate student Tzyy-Shyang Lin at MIT.

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