CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction

• URL: http://arxiv.org/abs/2506.12231v1
• Date: Fri, 13 Jun 2025 21:19:27 GMT
• Title: CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction
• Abstract summary: CheMixHub is a holistic benchmark for molecular mixtures.<n>It covers a corpus of 11 chemical mixtures property prediction tasks, from drug delivery formulations to battery electrolytes.<n>CheMixHub introduces various data splitting techniques to assess context-specific generalization and model robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 community. In this paper, we introduce CheMixHub, a holistic benchmark for molecular mixtures, covering a corpus of 11 chemical mixtures property prediction tasks, from drug delivery formulations to battery electrolytes, totalling approximately 500k data points gathered and curated from 7 publicly available datasets. CheMixHub introduces 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

Related papers

• Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction [8.367237766104159]
  We introduce Chem World, a comprehensive benchmark for chemical property prediction.<n>Chem World integrates 17 diverse chemical datasets with over 800,000 molecular samples.<n>We also propose Mixture-PINN, a physics-informed neural network based prediction framework.
  arXiv  Detail & Related papers  (2026-07-30T11:50:52Z)
• A Systematic Evaluation of Molecular Mixture Behavior Prediction [0.0]
  We propose an evaluation framework that decomposes mixture-property error into pure-compound and interaction (non-ideal) components.<n>We show that strong absolute accuracy can mask poor recovery of non-ideal mixture behavior.<n>These results identify transfer to unseen molecules as a central challenge in molecular mixture machine learning.
  arXiv  Detail & Related papers  (2026-05-28T09:58:59Z)
• Foundation Models for Discovery and Exploration in Chemical Space [57.97784111110166]
  MIST is a family of molecular foundation models trained on large unlabeled datasets.<n>We demonstrate the ability of these models to solve real-world problems across chemical space.
  arXiv  Detail & Related papers  (2025-10-20T17:56:01Z)
• Chemical knowledge-informed framework for privacy-aware retrosynthesis learning [72.39098405805318]
  Current machine learning-based retrosynthesis gathers reaction data from multiple sources into one single edge to train prediction models.<n>This paradigm poses considerable privacy risks as it necessitates broad data availability across organizational boundaries.<n>In the present study, we introduce the chemical knowledge-informed framework (CKIF), a privacy-preserving approach for learning retrosynthesis models.
  arXiv  Detail & Related papers  (2025-02-26T13:13:24Z)
• Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks [36.814181034608666]
  Surfactants are key ingredients in foaming and cleansing products across various industries. In practice, surfactant mixtures are typically used due to to performance, environmental, and cost reasons. We develop a graph neural network framework for surfactant mixtures to predict the temperature-dependent CMC.
  arXiv  Detail & Related papers  (2024-11-04T16:17:57Z)
• Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures [3.0478550046333965]
  We introduce a novel generic approach for improving data-driven models. We lump components that behave similarly into chemical classes and model them jointly. Using clustering leads to significantly improved predictions compared to an MCM without clustering.
  arXiv  Detail & Related papers  (2024-10-08T14:04:30Z)
• ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering [54.80411755871931]
  Question Answering (QA) effectively evaluates language models' reasoning and knowledge depth. Chemical QA plays a crucial role in both education and research by effectively translating complex chemical information into readily understandable format. This dataset reflects typical real-world challenges, including an imbalanced data distribution and a substantial amount of unlabeled data that can be potentially useful. We introduce a QAMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data.
  arXiv  Detail & Related papers  (2024-07-24T01:46:55Z)
• A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics [0.0]
  We present conditional Gaussian process models to predict ordinal outcomes from chemical experiments. A novel aspect of our model is that the kernel contains a scaling parameter, that controls the strength of the correlation between elements of the chemical space. We present a genetic algorithm for the facilitation of chemical discovery and identification of important features to the compound's efficacy.
  arXiv  Detail & Related papers  (2024-05-16T11:18:32Z)
• Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance [55.872926690722714]
  We study the predictability of model performance regarding the mixture proportions in function forms.<n>We propose nested use of the scaling laws of training steps, model sizes, and our data mixing law.<n>Our method effectively optimize the training mixture of a 1B model trained for 100B tokens in RedPajama.
  arXiv  Detail & Related papers  (2024-03-25T17:14:00Z)
• Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds [2.6803933204362336]
  We introduce various methods to detect and cluster chemical compounds based on their SMILES data. Our first method, analyzing the graphical structures of chemical compounds using embedding data, employs vector search to meet our threshold value. We also used natural language description embeddings stored in a vector database with GPT3.5, which outperforms the base model.
  arXiv  Detail & Related papers  (2023-10-25T18:00:24Z)
• Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning [0.3141085922386211]
  We develop a differentiable geometric deep learning model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation. We show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. With a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps.
  arXiv  Detail & Related papers  (2023-10-03T22:26:38Z)
• Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties [0.0]
  We introduce the elEmBERT model for chemical classification tasks. It is based on deep learning techniques, such as a multilayer encoder architecture. We demonstrate the opportunities offered by our approach on sets of organic, inorganic and crystalline compounds.
  arXiv  Detail & Related papers  (2023-09-17T19:41:32Z)
• QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules [69.25826391912368]
  We generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories. We show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules.
  arXiv  Detail & Related papers  (2023-06-15T23:39:07Z)
• Federated Learning of Molecular Properties in a Heterogeneous Setting [79.00211946597845]
  We introduce federated heterogeneous molecular learning to address these challenges. Federated learning allows end-users to build a global model collaboratively while preserving the training data distributed over isolated clients. FedChem should enable a new type of collaboration for improving AI in chemistry that mitigates concerns about valuable chemical data.
  arXiv  Detail & Related papers  (2021-09-15T12:49:13Z)
• Unassisted Noise Reduction of Chemical Reaction Data Sets [59.127921057012564]
  We propose a machine learning-based, unassisted approach to remove chemically wrong entries from data sets. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets.
  arXiv  Detail & Related papers  (2021-02-02T09:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.