The death of the Job plot, transparency, open science and online tools, uncertainty estimation methods and other developments in supramolecular chemistry data analysis

TLDR

Data analysis is central to understanding phenomena in host‑guest chemistry. The paper aims to discuss uncertainty estimation methods and to provide best‑practice protocols for data analysis in supramolecular chemistry. The authors use case studies and simulations to estimate uncertainties and demonstrate a web portal that promotes data access and methodological transparency. They show that the Job plot is unsuitable for most host‑guest problems, advocate systematic fitting of binding models, provide a web portal for data and software transparency, and present best‑practice protocols applicable to other chemical analyses.

Abstract

Data analysis is central to understanding phenomena in host-guest chemistry. We describe here recent developments in this field starting with the revelation that the popular Job plot method is inappropriate for most problems in host-guest chemistry and that the focus should instead be on systematically fitting data and testing all reasonable binding models. We then discuss approaches for estimating uncertainties in binding studies using case studies and simulations to highlight key issues. Related to this is the need for ready access to data and transparency in the methodology or software used, and we demonstrate an example a webportal () that aims to address this issue. We conclude with a list of best-practice protocols for data analysis in supramolecular chemistry that could easily be translated to other related problems in chemistry including measuring rate constants or drug IC50 values.