AI Tool Streamlines Drug Synthesis

Machine learning model predicts outcomes of complex chemical reactions to speed up drug discovery

Published on Mar. 10, 2026

Got story updates? Submit your updates here. ›

Researchers have developed a machine learning-based system that can rapidly predict the outcomes of complex chemical reactions used in drug discovery, potentially saving time and money in the drug development process. The tool focuses on asymmetric cross-coupling reactions, which are crucial for producing the correct "handedness" of drug molecules, and can make accurate predictions based on limited training data.

Why it matters

Drug discovery is an incredibly time-consuming and expensive process, often requiring extensive trial-and-error experimentation to find the right molecular structures. This new AI-powered tool could significantly streamline this process by allowing chemists to quickly screen thousands of potential reactions and identify the most promising paths forward, cutting down on the amount of physical lab work required.

The details

The researchers trained their machine learning model on data from just a few published studies on asymmetric cross-coupling reactions, which are used to build complex drug molecules. The model was then able to accurately predict the outcomes of hypothetical reactions involving different catalysts, ligands, and substrates - even those that were quite different from the original training data. This allows chemists to rapidly explore a much wider chemical space and identify the most promising routes for further investigation, potentially saving weeks or months of lab work.

  • The study was published as an accelerated preview in the journal Nature on February 11, 2026.

The players

Simone Gallarati

The study's co-lead author and joint postdoctoral researcher at the University of Utah and the University of California, Los Angeles.

Erin Bucci

The study's co-lead author and doctoral student at the University of California, Los Angeles.

Matthew Sigman

A chemist at the University of Utah and coauthor of the study.

Abigail Doyle

A chemist at the University of California, Los Angeles and coauthor of the study.

Got photos? Submit your photos here. ›

What they’re saying

“Sometimes 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. We wanted to train statistical models that were 'smart' enough to make accurate predictions on untested reactions, but also as cheap as possible.”

— Simone Gallarati, Co-lead author (Mirage News)

“Most AI requires enormous amounts of data to train models on. That's a problem in chemistry by which obtaining high-quality, large datasets from experimental work is very expensive and extremely time consuming. The 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't seen yet.”

— Matthew Sigman, Chemist (Mirage News)

“As a lab-based chemist, this tool is extremely valuable for saving time spent running experiments. For 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-this tool greatly cuts the amount of money I would typically spend on materials.”

— Erin Bucci, Co-lead author (Mirage News)

What’s next

The researchers plan to continue refining and expanding the capabilities of the machine learning model to make it an even more powerful tool for drug discovery.

The takeaway

This new AI-powered system represents a significant advance in the field of computational chemistry, demonstrating how machine learning can be leveraged to dramatically streamline the drug discovery process and reduce the time and cost associated with developing new medicines.