# Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning

arXiv: https://arxiv.org/abs/2502.12049 · HTML: https://mlsanity.com/paper/2502.12049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)

## Built on by (most cited first, 1 of 1)

- [Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives](https://mlsanity.com/paper/2504.03847/) (2025)

## Abstract (arXiv)

Virus-like particles (VLPs) are valuable for vaccine development due to their immune-triggering properties. Understanding their stoichiometry, the number of protein subunits to form a VLP, is critical for vaccine optimisation. However, current experimental methods to determine stoichiometry are time-consuming and require highly purified proteins. To efficiently classify stoichiometry classes in proteins, we curate a new dataset and propose an interpretable, data-driven pipeline leveraging linear machine learning models. We also explore the impact of feature encoding on model performance and interpretability, as well as methods to identify key protein sequence features influencing classification. The evaluation of our pipeline demonstrates that it can classify stoichiometry while revealing protein features that possibly influence VLP assembly. The data and code used in this work are publicly available at https://github.com/Shef-AIRE/StoicIML.