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Predicting Macrocyclic Molecular Recognition with Machine Learning

13 August 2019, Version 1
Working Paper
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

DFT, NMR, ITC, and cell confluence data are used to generate predictive algorithms of supramolecular binding to cucurbit[7]uril and experimentally validate these predictions.

Keywords

Machine learning
Supramolecular interactions
Cucurbituril

Supplementary materials

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Now Published

Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit[7]uril [opens in a new tab]
Anthony Tabet, Thomas Gebhart, Guanglu Wu, Charlie Readman, Merrick Pierson Smela, Vijay K. Rana, Cole Baker, Harry Bulstrode, Polina Anikeeva, David H. Rowitch, Oren A. Scherman journal article
Physical Chemistry Chemical Physics , Volume 22, Issue 26
Online publication date: 2020

Version History

Aug 13, 2019 Version 1

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The content is available under CC BY NC ND 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv.9540068.v1
D O I: 10.26434/chemrxiv.9540068.v1 [opens in a new tab]

Author’s competing interest statement

None

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