IQS-URL en Barcelona será la sede de la primera escuela práctica de resonancia magnética nuclear / IQS-URL in Barcelona will be the headquarters of the first practical school of nuclear magnetic resonance @jeoleurope.bsky.social www.rrhhpress.com/desarrollo/6... #NMRnews #NMRchat 🧲
21.11.2025 14:03
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Molecular Drawing in JASON
YouTube video by JEOL JASON NMR Software
www.youtube.com/watch?v=wkuQ...
JASON can be used for drawing molecules, NMR prediction, etc.
08.11.2025 09:49
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We are pleased to announce the launch of the 1st School for Practical Insights in NMR (SPINMR).
📅 21-23 January 2026
📌 IQS Barcelona
A scientific gathering event dedicated to real-world applications and the practical aspects of #NMR.
Register here 👉 lnkd.in/eip9Yn3j
24.07.2025 14:08
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My dream come true! 🌟🤩 our new 600 MHz NMR is finally here! 🤯
I really can't believe it! This is a huge technological leap for the region, for the UCLM and for all research, including my group's! 🙌
I feel so thankful 🙏 @erc.europa.eu @uclm.es @gobjccm.bsky.social
Never stop dreaming! 💫
29.04.2025 22:17
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From NMR to AI: Do We Need 1H NMR Experimental Spectra to Obtain High-Quality logD Prediction Models?
This study presents a novel approach to 1H NMR-based machine learning (ML) models for predicting logD using computer-generated 1H NMR spectra. Building on our previous work, which integrated experimental 1H NMR data, this study addresses key limitations associated with experimental measurements, such as sample stability, solvent variability, and extensive processing, by replacing them with fully computational workflows. Benchmarking across various density functional theory (DFT) functionals and basis sets highlighted their limitations, with DFT-based models showing relatively high RMSE values (average CHI logD of 1.12, lowest at 0.96) and extensive computational demands, limiting their usefulness for large-scale predictions. In contrast, models trained on predicted 1H NMR spectra by NMRshiftDB2 and JEOL JASON achieved RMSE values as low as 0.76, compared to 0.88 for experimental spectra. Further analysis revealed that mixing experimental and predicted spectra did not enhance accuracy, underscoring the advantage of homogeneous datasets. Validation with external datasets confirmed the robustness of our models, showing comparable performance to commercial software like Instant JChem, thus underscoring the reliability of the proposed computational workflow. Additionally, using normalized RMSE (NRMSE) proved essential for consistent model evaluation across datasets with varying data scales. By eliminating the need for experimental input, this workflow offers a widely accessible, computationally efficient pipeline, setting a new standard for ML-driven chemical property predictions without experimental data constraints.
Leniak et al. show that predicted 1H NMR spectra can outperform experimental data for logD modeling. By training Gradient Boosting on simulated vs. real spectra, they find fully generated sets often yield the most accurate lipophilicity predictions. pubs.acs.org/doi/full/10....
07.03.2025 10:44
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Careers at JEOL (U.K.) LTD.
If you enjoy tinkering with NMR, this NMR applications position based in UK may be for you: www.jeoluk.com/Careers
#chemjobs #ukchemjobs #chemsky #NMR #NMRchat
09.02.2025 20:40
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Careers at JEOL (U.K.) LTD.
JEOL UK is hiring a new NMR applications specialist to be based in our newly refurbished NMR demonstration laboratory in Welwyn Garden City.
A full description of the post is available at www.jeoluk.com/Careers
For any queries, you can drop me a message.
#NMR #NMRchat
29.01.2025 20:30
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This Wednesday 22 at 4 PM CET Arkadiusz Leniak from Celon Pharma will talk about how to predict physicochemical properties such as logD using NMR spectra and machine learning. Register at register.gotowebinar.com/register/728...
#nmr #nmrchat #chemsky #nmrevents
20.01.2025 09:05
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Pumping iron 🎈
06.12.2024 17:22
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Tomorrow's webinar on NMR of quadrupolar nuclei
25.11.2024 12:22
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