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1. 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations

   https://doi.org/10.1093/bioinformatics/btad354  

   Hong, Yuhui; Li, Sujun; Welch, Christopher J; Tichy, Shane; Ye, Yuzhen; Tang, Haixu (June 2023, Bioinformatics)

   Elofsson, Arne (Ed.)

   Abstract Motivation Tandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years, in silico methods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds’ 3D conformations, and thus neglected critical structural information. Results We present the 3D Molecular Network for Mass Spectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.691 and 0.478 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification. Availability and implementation The codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org. 

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2. Instrument-agnostic Machine Learning Framework for Accurate Prediction of Functional Groups from Tandem Mass Spectrometry

   https://doi.org/10.26434/chemrxiv-2025-px8nb  

   Beveridge, Connor; Wijewardhane, Prageeth; Muhoberac, Matthew; Morato, Nicolas; Kaerner, Andreas; Zhang, Li-Kang; Richardson, Douglas; Shoulds, Ayanna; Gilbert, Jeffrey; Welch, Christopher; et al (September 2025, ChemRxiv)

   Structural elucidation of unknown compounds using tandem mass spectrometry (MS/MS) is an ongoing challenge. Expert chemists will often use common product ion peaks and neutral losses or peak matching software to predict structural aspects from MS/MS spectra, a process which is time consuming and limited by the molecular entries in the database. We introduce an instrument-agnostic machine learning (ML) framework that predicts functional groups directly from MS/MS spectra, eliminating the need for conventional database searches. The ML models are trained on data from different sources, the Mass Bank of North America (MoNA), a public database, and a custom MS/MS data-base acquired using a single high throughput desorption electrospray ionization (DESI) MS/MS platform to showcase transferability of model predictions independent of instrument acquisition. Additionally, a novel spectral representa-tion is developed for ML use by standardizing the spectra to the precursor ion m/z. We utilized molecular metrics, specifically, average molecular F1 score of 87% (MoNA) and 76% (DESI) and average molecular accuracy of 94% (MoNA) and 87% (DESI), indicating similar model performance when trained on different databases to validate the ML framework. To further illustrate the robustness and transferability of our ML framework, models trained on DESI-generated data successfully predicted functional groups, using spectral data from blind test set of molecules that were generated by two different company laboratories using distinct mass spectrometry instruments (Orbitrap and TOF) operated in positive-ion mode electrospray ionization. We believe that such an approach, where models trained on data from one instrument can reliably make accurate predictions on spectra acquired using different instruments in a database-free manner, will significantly expand the practical applications of ML to mass spectrometry. 

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3. Lithospheric mantle xenolith geochemistry from south-central Vietnam: Trace elements

   https://doi.org/10.26022/IEDA/112849  

   Hobbs, Kirby; Elkins, Lynne Elkins; Lassiter, John Lassiter; Hoang, Nguyen; Burberry, Caroline Burberry (January 2023, Interdisciplinary Earth Data Alliance (IEDA))

   Trace element data measured by LA-ICP-MS analysis in crystal separates for lithospheric mantle xenoliths from south-central Vietnam. Samples are peridotite xenoliths from two alkali basalt locations in Vietnam, Pleiku and Xuan Loc. Data are trace elements measured by LA-ICP-MS in mineral separates. 

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4. An estimate of the mass of the Milky Way from the Magellanic Stream

   https://doi.org/10.1093/mnras/stac2308  

   Craig, Peter A; Chakrabarti, Sukanya; Baum, Stefi; Lewis, Benjamin T (August 2022, Monthly Notices of the Royal Astronomical Society)

   Abstract We present a model for the formation of the Magellanic Stream (MS) due to ram pressure stripping. We model the history of the Small and Large Magellanic Clouds in the recent cosmological past in a static Milky Way potential with diffuse halo gas, using observationally motivated orbits for the Magellanic Clouds derived from HST proper motions within the potential of the Milky Way. This model is able to reproduce the trailing arm but does not reproduce the leading arm feature, which is common for models of the stream formation that include ram pressure stripping effects. While our model does not outperform other models in terms of matching the observable quantities in the MS, it is close enough for our ultimate goal – using the MS to estimate the MW mass. From analyzing our grid of models, we find that there is a direct correlation between the observed stream length in our simulations and the mass of the Milky Way. For the observed MS length, the inferred Milky Way mass is 1.5 ± 0.32 × 1012 M⊙, which agrees closely with other independent measures of the Milky Way mass. We also discuss the MS in the context of HI streams in galaxy clusters, and find that the MS lies on the low-mass end of a continuum from Hickson groups to the Virgo cluster. As a tracer of the dynamical mass in the outer halo, the MS is a particularly valuable probe of the Milky Way’s potential. 

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5. Mean Zonal Drift Velocities of Plasma Bubbles Estimated from Keograms of Nightglow All-Sky Images from the Brazilian Sector

   https://doi.org/10.3390/atmos11010069  

   Vargas, Fabio; Brum, Christiano; Terra, Pedrina; Gobbi, Delano (January 2020, Atmosphere)

   We present in this work a method for estimation of equatorial plasma bubble (EPB) mean zonal drift velocities using keograms generated from images of the OI 6300.0 nm nightglow emission collected from an equatorial station–Cariri (7.4° S, 36.5° W), and a mid-latitude station–Cachoeira Paulista (22.7° S, 45° W), both in the Brazilian sector. The mean zonal drift velocities were estimated for 239 events recorded from 2000 to 2003 in Cariri, and for 56 events recorded over Cachoeira Paulista from 1998 to 2000. It was found that EPB zonal drift velocities are smaller (≈60 ms−1) for events occurring later in the night compared to those occurring earlier (≈150 ms−1). The decreasing rate of the zonal drift velocity is ≈10 ms−1/h. We have also found that, in general, bubble events appearing first in the west-most region of the keograms are faster than those appearing first in the east-most region. Larger zonal drift velocities occur from 19 to 23 LT in a longitude range from −37° to −33°, which shows that the keogram method can be used to describe vertical gradients in the thermospheric wind, assuming that the EPBs drift eastward with the zonal wind. The method of velocity estimation using keograms compares favorably against the mosaic method developed by Arruda, D.C.S, 2005, but the standard deviation of the residuals for the zonal drift velocities from the two methods is not small (≈15 ms−1). 

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