# Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data

arXiv: https://arxiv.org/abs/2207.02980 · HTML: https://mlsanity.com/paper/2207.02980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding](https://mlsanity.com/paper/2106.02795/) (2021)
- [DeepNovoV2: Better de novo peptide sequencing with deep learning](https://mlsanity.com/paper/1904.08514/) (2019)

## Built on by (most cited first, 2 of 2)

- [Prefix-Tree Decoding for Predicting Mass Spectra from Molecules](https://mlsanity.com/paper/2303.06470/) (2023)
- [Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond](https://mlsanity.com/paper/2502.09897/) (2025)

## Abstract (arXiv)

Small molecules in biological samples are studied to provide information about disease states, environmental toxins, natural product drug discovery, and many other applications. The primary window into the composition of small molecule mixtures is tandem mass spectrometry (MS2), which produces data that are of high sensitivity and part per million resolution. We adopt multi-scale sinusoidal embeddings of the mass data in MS2 designed to meet the challenge of learning from the full resolution of MS2 data. Using these embeddings, we provide a new state of the art model for spectral library search, the standard task for initial evaluation of MS2 data. We also introduce a new task, chemical property prediction from MS2 data, that has natural applications in high-throughput MS2 experiments and show that an average R2 of 80% for novel compounds can be achieved across 10 chemical properties prioritized by medicinal chemists. We use dimensionality reduction techniques and experiments with different floating point resolutions to show the essential role multi-scale sinusoidal embeddings play in learning from MS2 data.