CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction
Ella Miray Rajaonson, Mahyar Rajabi Kochi, Luis M. Mejía-Mendoza, Seyed Mohamad Moosavi, Benjamin Manuel Sanchez
Abstract
Developing improved predictive models for multi-molecular systems is crucial, as nearly every chemical product used results from a mixture of chemicals. While being a vital part of the industry pipeline, the chemical mixture space remains relatively unexplored by the Machine Learning (ML) community. In this paper, we introduce CheMixHub, a holistic benchmark for molecular mixtures spanning a corpus of 11 chemical mixtures property prediction tasks. With applications ranging from drug delivery formulations to battery electrolytes, CheMixHub currently totals approximately 500k data points gathered and curated from 7 publicly available datasets. We devise various data splitting techniques to assess context-specific generalization and model robustness, providing a foundation for the development of predictive models for chemical mixture properties. Furthermore, we map out the modelling space of deep learning models for chemical mixtures, establishing initial benchmarks for the community. This dataset has the potential to accelerate chemical mixture development, encompassing reformulation, optimization, and discovery. The dataset and code for the benchmarks can be found at: https://github.com/chemcognition-lab/chemixhub
BibTeX
@inproceedings{
rajaonson2025chemixhub,
title={CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction},
author={Ella Miray Rajaonson and Mahyar Rajabi Kochi and Luis M. Mej{\'\i}a-Mendoza and Seyed Mohamad Moosavi and Benjamin Manuel Sanchez},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025},
url={https://openreview.net/forum?id=8HUnx0rJNq}
}