ArticleAnalytical chemistry2025
pyIHM: Indirect Hard Modeling, in Python.
Article in Analytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- High-Resolution Relaxometry for Fragment Screening.Journal of medicinal chemistry · 2026Article
- Extracting Trends From NMR Data With TrAGICo: A Python Toolbox.Magnetic resonance in chemistry : MRC · 2025Article
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Authors and funding
6 authors.
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Abstract
NMR is a powerful analytical technique that combines an exquisite qualitative power, related to the unicity of the spectra of each molecule in a mixture, with an intrinsic quantitativeness, related to the fact that the integral of each peak only depends on the number of nuclei (i.e., the amount of substance times the number of equivalent nuclei in the signal), regardless of the molecule. Signal integration is the most common approach in quantitative NMR but has several drawbacks (vide infra). An alternative is to use hard modeling of the peaks. In this paper, we present pyIHM, a Python package for the quantification of the components of NMR spectra through indirect hard modeling, and we discuss some numerical details of the implementation that make this approach robust and reliable.
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Registered trials
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