Machine Learning-Based Rapid Multi-Component Quantification in Danshen Injections Using 1h Nmr DOI

Xinyuan Xie,

Sijun Wu, Jiayu Yang

et al.

Published: Jan. 1, 2024

Language: Английский

Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates DOI Creative Commons
Jules Schleinitz, Alba Carretero‐Cerdán, Anjali Gurajapu

et al.

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: 147(9), P. 7476 - 7484

Published: Feb. 21, 2025

The development of machine learning models to predict the regioselectivity C(sp3)-H functionalization reactions is reported. A data set for dioxirane oxidations was curated from literature and used generate a model C-H oxidation. To assess whether smaller, intentionally designed sets could provide accuracy on complex targets, series acquisition functions were developed select most informative molecules specific target. Active learning-based that leverage predicted reactivity uncertainty found outperform those based molecular site similarity alone. use elaboration significantly reduced number points needed perform accurate prediction, it machine-designed can give predictions when larger, randomly selected fail. Finally, workflow experimentally validated five substrates shown be applicable predicting arene radical borylation. These studies quantitative alternative intuitive extrapolation "model substrates" frequently estimate molecules.

Language: Английский

Citations

2
Machine learning-based rapid multi-component quantification in Danshen Injections using 1H NMR DOI

Xinyuan Xie,

Sijun Wu, Jiayu Yang

et al.

Measurement, Journal Year: 2025, Volume and Issue: 248, P. 116957 - 116957

Published: Feb. 7, 2025

Language: Английский

Citations

1
Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI DOI
Jakob K. Reinhardt, David Craft, Jing‐Ke Weng

et al.

Trends in Biochemical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

1
pyIHM: Indirect Hard Modeling, in Python DOI Creative Commons
F. Bruno, Letizia Fiorucci, Alessia Vignoli

et al.

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

NMR is a powerful analytical technique that combines an exquisite qualitative power, related to the unicity of spectra each molecule in mixture, with intrinsic quantitativeness, fact integral peak only depends on number nuclei (i.e., amount substance times equivalent signal), regardless molecule. Signal integration most common approach quantitative but has several drawbacks (vide infra). An alternative use hard modeling peaks. In this paper, we present pyIHM, Python package for quantification components through indirect modeling, and discuss some numerical details implementation make robust reliable.

Language: Английский

Citations

0
Machine Learning-Based Rapid Multi-Component Quantification in Danshen Injections Using 1h Nmr DOI

Xinyuan Xie,

Sijun Wu, Jiayu Yang

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0