\u201cSometimes we use sophisticated, physics-based computational chemistry tools to understand novel reactions. However, these tools are too expensive to make predictions on thousands of potential new molecules,\u201d said Simone Gallarati, the study\u2019s co-lead author and joint postdoctoral researcher at the University of Utah and the University of California, Los Angeles. \u201cWe wanted to train statistical models that were \u2018smart\u2019 enough to make accurate predictions on untested reactions, but also as cheap as possible.\u201d<\/p>\n
Molecules can exist as mirror images, a property known as \u201chandedness.\u201d Left- versus right-handed forms are crucial; one may heal, the other might harm. Chemists need to find just the right set of tools\u2014catalysts, ligands and substrates\u2014to ensure they construct the correct version.<\/p>\n The new system acts as a high-tech filter that can screen tens of thousands of chemical structures to predict how the pieces will come together to produce one \u201chand\u201d of a molecule over another. The workflow provides a cost-effective way to convert the reaction\u2019s components into numerical data a computer can analyze, building the framework for machine-learning predictions.<\/p>\n With surprisingly little input the model reliably forecasted how the components would behave, cutting down the time, energy, and expense spent testing reactions in the lab.<\/p>\n \u201cMost AI requires enormous amounts of data to train models on. That\u2019s a problem in chemistry by which obtaining high-quality, large datasets from experimental work is very expensive and extremely time consuming,\u201d said Matthew Sigman<\/a>, chemist at the U and coauthor of the study. \u201cThe coolest thing about this tool is that it allows someone to collect smaller bits of data, build reasonably good models and make accurate predictions for known reactions, and also transfer predictions to reactions that the models haven\u2019t seen yet.\u201d<\/p>\n The study was published as an accelerated preview in the journal Nature<\/a> on Feb. 11, 2026.<\/p>\n High-tech filter<\/strong> The researchers centered the workflow around asymmetric cross-coupling reactions, a powerful toolkit for drug development. The reactions stitch together two carbon-based molecular fragments, using a metal catalyst to build more complex compounds. The reactions are called asymmetric because they\u2019re designed to favor one \u201chanded\u201d version of the molecule. Chemists often produce both versions but without guidance, the experiments will yield a 50\/50 split. In contrast, asymmetric reactions deliver, say, 95% of the desired form and just 5% of the unwanted mirror image.<\/p>\n Asymmetric cross-coupling reactions generally require at least three elements\u2014a metal, a ligand and substrates. The metal catalyst does the heavy lifting by joining carbon-based molecules to build the product. A ligand binds to the metal, controlling which side of the molecule reacts, influencing the three-dimensional orientation of the product. The ligand is arguably the most important element to control a molecule\u2019s handedness.<\/p>\n To train their model, Gallarati and the team identified four academic papers on asymmetric reactions\u2014coauthor Abigail Doyle\u2019s and Sigman\u2019s past work included\u2014that all used nickel-based catalysts with different ligands. Those results were the only training data for the workflow. Then, the team asked the system to predict the outcomes of hypothetical components not included in the training data. They added a series of increasingly challenging tasks that forced the algorithm to make predictions with materials that were increasingly dissimilar to the original training data. The team tested the prediction in the Doyle lab, an effort led by Erin Bucci, the study\u2019s co-lead author and doctoral student at UCLA.<\/p>\n \u201cAs a lab-based chemist, this tool is extremely valuable for saving time spent running experiments,\u201d Bucci said. \u201cFor example, instead of running 50-60 reactions, we are now able to run 5-10, potentially saving weeks or months. Each reaction component we test in the lab needs to either be purchased or made from scratch\u2014this tool greatly cuts the amount of money I would typically spend on materials.\u201d<\/p>\n While the authors tested the tool in the context of new nickel-based reactions, the workflow can apply across fields and even deepen our understanding of chemistry itself.<\/p>\n![\"A](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2026\/03\/Sigman_Handedness-e1773074264459.png\")
<\/a>Many molecules used in medicines have a symmetry known as handedness; they have the same atoms connected in the same order, but the 3-D arrangement is mirrored and cannot be superimposed. The body reacts very differently to the \u201cright-\u201d and \u201cleft-\u201d handed versions of the molecule. Credit: Erin Bucci\/UCLA<\/p>\n
\n![\"Two](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2026\/03\/Sigman_simone_Erin_WEB-e1773073660648.jpg\")
<\/a>The study\u2019s co-lead authors Simon Gallarati (left) and Erin Bucci (right). Credit: Madeline Ruos\/UCLA<\/p>\n![\"Ten](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2026\/03\/Sigman_vials.jpeg\")
<\/a>Vials containing different combinations of nickel, chiral ligands, and substrates to be tested based on the machine learning model\u2019s suggestions. Credit: Erin Bucci\/UCLA<\/p>\n