为什么有机化学可能有助于构建能解释其答案的AI

September 3, 2026 Why organic chemistry may help build AI that can explain its answers by Brandi Wampler, University of Notre Dame edited by Gaby Clark, reviewed by Robert Egan Gaby Clark Scientific Editor Meet our editorial team Behind our editorial process Robert Egan Senior Editor Meet our editorial team Behind our editorial process Editors' notes This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked peer-reviewed publication trusted source proofread The GIST Add as preferred source Visual Abstract. Credit: Chemical Reviews (2026). DOI: 10.1021/acs.chemrev.5c01081 While most believe artificial intelligence (AI) is changing science, researchers at the University of Notre Dame are exploring the reverse. In an article published in Chemical Reviews, AI and chemistry experts discussed how the intricacies of organic chemistry are changing the way researchers design AI technology, paving the way for a new generation of development. "Organic chemistry presents a particularly demanding set of problems for AI," said Nitesh Chawla, lead author of the article and the Frank M. Freimann Professor of Computer Science and Engineering. Chawla also serves as the Lucy Family Director for Data & AI Academic Strategy, leading the Data, AI, and Computing Initiative. "Those challenges have driven important advances in how AI represents complex problems, reasons from limited evidence, and accounts for uncertainty. In that sense, chemistry has been a catalyst for better AI—systems that can directly benefit human health, safety, and well-being." Chemistry pushes AI past prediction For AI models to analyze chemistry properly, they must be able to consider several factors, such as complex three-dimensional chemical structures, multiple interacting components, changing conditions and more than one potential outcome. But chemists need AI to do more than predict the properties of a compound or the outcome of a reaction. They want to understand why results occur, the mechanisms involved, what conditions matter for those results and how confident they should be in the prediction. "When you ask a model to evaluate molecular representations or reaction variables, you are asking it to reason, which forces AI to move beyond black-box predictions. This demand encourages the development of complete systems that are interpretable, respect the laws of chemistry and physics, and connect predictions to established scientific principles," said Olaf Wiest, co-author of the article and the Grace-Rupley Professor of Chemistry and Biochemistry. Wiest is also the founder and lead principal investigator of the National Science Foundation Center for Computer-Assisted Synthesis. Limited data changes the approach To complicate matters further, improving today's AI models typically relies on scale—larger models, more computing power and more data. In organic chemistry, high-quality experimental data is limited, often coming from a mix of sources in different formats and structures. In short, chemistry has challenged AI researchers to think outside the box and innovate because they cannot rely on scale alone to improve their models. "These challenges are not unique to chemistry, but chemistry makes them impossible to avoid. Extracting reliable information from limited and imperfect evidence requires us to think more critically about how problems are represented, what prior knowledge can be incorporated, and how much confidence we should place in a prediction," Chawla said. Testing ground for scientific AI These developments are essential for advances in AI technologies that could support or even fast-track the many areas that depend on the "central science," chemistry. For example, self-driving laboratories—automated systems that can execute scientific experiments with minimal human intervention—could accelerate the discovery of new materials and drugs. These agentic systems will need the ability to independently formulate hypotheses, design and perform experiments, interpret results and improve their processes. Taken together, Chawla said this makes organic chemistry a powerful environment for developing better AI systems that integrate transparent protocols, rigorous validation and safety constraints. "The future of scientific AI will likely be hybrid, bringing together statistical learning, symbolic and mechanistic knowledge, human expertise, and experimental feedback," Chawla said. "What we learn by solving these problems in organic chemistry can make AI systems more reliable, more efficient, and better equipped to serve science and society." Publication details Nitesh V. Chawla et al, Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms, Chemical Reviews (2026). DOI: 10.1021/acs.chemrev.5c01081 Journal information: Chemical Reviews Key concepts Large language modelsAI alignmentTrustworthy machine learningGenerative AI ethicsMachine learning methodologies Provided by University of Notre Dame Who's behind this story? Gaby Clark MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile → Robert Egan Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile → Citation: Why organic chemistry may help build AI that can explain its answers (2026, September 3) retrieved 8 September 2026 from https://techxplore.com/news/2026-09-chemistry-ai.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. 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