Hemoglobin, which gives blood its red color, the light-emitting materials in smartphone OLED displays, and catalysts used in manufacturing plastics and pharmaceuticals—all share a common feature that is they all involve metal–ligand coordination complexes. In these structures, a central metal ion is surrounded by organic molecules, known as ligands. Now, a novel artificial intelligence (AI) algorithm offers the promise of designing these complex materials more swiftly and accurately, significantly reducing trial-and-error efforts.

Predicting how a metal ion will 'shake hands' with an organic molecule is an inherently complex task, often challenging even for seasoned scientists. This difficulty arises because organic ligands contain multiple potential binding sites, and the number and mode of coordination can vary widely depending on the specific metal and its oxidation state. As a result, dozens or even hundreds of potential structural configurations can exist.

Led by Distinguished Professor Bartosz A. Grzybowski of the Department of Chemistry and Director of the Center for Algorithmic and Robotized Synthesis (CARS) within the Institute for Basic Science (IBS) at UNIST, the research team has addressed this challenge not through increased computational power, but by integrating 'chemical intuition' directly into AI. Published in Angewandte Chemie, their hybrid AI approach moves beyond simple pattern recognition by encoding fundamental chemical principles into neural networks.

Figure 1. An AI-powered hybrid model predicts metal–ligand coordination modes by integrating chemical rules from crystal structures with machine learning, enabling accurate design of complex organometallic structures.

"We have developed a 'hybrid' strategy that combines classical chemical knowledge with machine learning to accurately predict how metals and organic ligands bind," explains Professor Grzybowski. "By analyzing over 100,000 crystal structures from the Cambridge Structural Database, we trained a model that 'understands' the specific behavior of different metals and their oxidation states—something previous models could not achieve."

This innovative technology is poised to become a key component in large-scale computational pipelines. Professor Grzybowski notes, “We expect it to be highly valuable in designing transition metal catalysts used across industries—from pharmaceuticals to advanced functional materials.”

To make this cutting-edge tool accessible worldwide, the team has released RDMetallics, an open-source, Python-based software package, along with a dedicated web portal (coordinate.rdmetallics.net). Researchers anywhere in the world can now predict complex metal–ligand coordination modes directly through their web browsers—no coding skills or expensive hardware required.

Supported by the Institute for Basic Science (IBS), this research was published online on Angewandte Chemie International Edition on February 21, 2026.

Bartosz A. Grzybowski
Distinguished Professor, Department of Chemistry, UNIST
Director, IBS Center for Robotized and Algorithmic Synthesis (CARS)
E: grzybor72@unist.ac.kr

William I. Suh
IBS Public Relations Team
E: willisuh@ibs.re.kr

Story Source
Materials provided by the Institute of Basic Science.

Journal Reference
Galymzhan Moldagulov, Kisung Lee, Sanzhar Nurgaliyev, et al., "Hybrid Computational Strategy for Predicting Complex Ligand–Metal Architectures," Angew. Chem. Int. Ed. (2026). https://doi.org/10.1002/anie.202524655