Jump to main content
Jump to site search
SCHEDULED MAINTENANCE Close the message box

Maintenance work is planned for Monday 16 August 2021 from 07:00 to 23:59 (BST).

Website performance may be temporarily affected and you may not be able to access some PDFs or images. If this does happen, refreshing your web browser should resolve the issue. We apologise for any inconvenience this might cause and thank you for your patience.


Issue 26, 2020

Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit[7]uril

Author affiliations

Abstract

Machine learning is a valuable tool in the development of chemical technologies but its applications into supramolecular chemistry have been limited. Here, the utility of kernel-based support vector machine learning using density functional theory calculations as training data is evaluated when used to predict equilibrium binding coefficients of small molecules with cucurbit[7]uril (CB[7]). We find that utilising SVMs may confer some predictive ability. This algorithm was then used to predict the binding of drugs TAK-580 and selumetinib. The algorithm did predict strong binding for TAK-580 and poor binding for selumetinib, and these results were experimentally validated. It was discovered that the larger homologue cucurbit[8]uril (CB[8]) is partial to selumetinib, suggesting an opportunity for tunable release by introducing different concentrations of CB[7] or CB[8] into a hydrogel depot. We qualitatively demonstrated that these drugs may have utility in combination against gliomas. Finally, mass transfer simulations show CB[7] can independently tune the release of TAK-580 without affecting selumetinib. This work gives specific evidence that a machine learning approach to recognition of small molecules by macrocycles has merit and reinforces the view that machine learning may prove valuable in the development of drug delivery systems and supramolecular chemistry more broadly.

Graphical abstract: Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit[7]uril

Supplementary files

Article information


Submitted
24 Oct 2019
Accepted
16 Jun 2020
First published
22 Jun 2020

This article is Open Access

Phys. Chem. Chem. Phys., 2020,22, 14976-14982
Article type
Paper

Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit[7]uril

A. Tabet, T. Gebhart, G. Wu, C. Readman, M. Pierson Smela, V. K. Rana, C. Baker, H. Bulstrode, P. Anikeeva, D. H. Rowitch and O. A. Scherman, Phys. Chem. Chem. Phys., 2020, 22, 14976 DOI: 10.1039/C9CP05800A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.


Social activity

Search articles by author

Spotlight

Advertisements