ArticleCommunications chemistry2025
Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- Fine-tuning large language models to generate single-atom catalyst synthesis procedures.Communications chemistry · 2026Article
- Bayesian Optimization of Catalysis with In-Context Learning.ACS central science · 2026Article
- Interpretable QSAR and Complementary Docking for PARP1 Inhibitor Prioritization: Reliability Stratification and Near-Domain Screening.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Bridging electron microscopy and materials analysis with an autonomous agentic platform.Science advances · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Machine learning-derived stage-specific design rules for metal-organic framework selection in seasonal hydrogen storage.Scientific reports · 2026Article
- Two-dimensional layered metal oxides (2D LMOs) for next-generation electronic devices.Nanoscale advances · 2026Review
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.