Seyedsayamdost receives 2025 Schmidt Transformative Technology Funding

Professor of Chemistry Mohammad Seyedsayamdost has been named a funding recipient for one of four research projects — focused on small molecule structure determination, sustainable energy storage, natural hydrogen gas generation, and tropical marine ecosystem mitigation — announced this week through the Eric and Wendy Schmidt Transformative Technology Fund.

Seyedsayamdost receives funding jointly with Ellen Zhong, assistant professor of computer science, for their work on a new AI system to transform how scientists identify small molecules.

The goal of the Technology Fund is to enable researchers to make leaps rather than incremental advances in the natural sciences and engineering. It supports projects that lead to the invention of a disruptive new technology that can have a major impact on a field of research, or to the development of equipment or an enabling technology that will transform research in a field.

The fund was created in 2009 through a gift from Eric and Wendy Schmidt. Eric Schmidt is Executive Chairman and CEO of Relativity Space, co-founder of Schmidt Sciences, The Schmidt Family Foundation, and Schmidt Ocean Institute, the former Chief Executive Officer of Google, and former Executive Chairman of Alphabet Inc., Google’s parent company. Wendy Schmidt is co-founder of Schmidt Sciences, and president and co-founder of The Schmidt Family Foundation and Schmidt Ocean Institute. Eric Schmidt earned his bachelor’s degree in electrical engineering from Princeton in 1976 and served as a Princeton Trustee from 2004 to 2008.

“Across engineering, computer science, chemistry and geosciences, the research teams supported by the Schmidt fund are developing novel solutions to persistent, thorny problems,” said Princeton University Dean for Research Peter Schiffer, vice president for the Princeton Plasma Physics Laboratory and Class of 1909 Professor of Physics. “Their work holds great promise to spark transformative change that will make a meaningful difference in people’s lives and for our collective future.”

The winning proposals were selected by an anonymous panel of faculty reviewers.

AI and the structure of small molecules

Determining the 3-D structure of small molecules, a class that includes hormones, vitamins, and most FDA-approved drugs, is essential for understanding how they function and interact in cells. However, current techniques require painstaking and time-consuming experimentation. In this project, the researchers seek to develop an algorithm that can automate the process of determining small molecule structure from nuclear magnetic resonance (NMR) spectral analysis.

In preliminary work, the researchers developed a machine learning algorithm that reliably translated one-dimensional NMR spectra of five-residue peptides into precise molecular structures. The current project will expand this approach to include broad classes of small molecules of various sizes and structure types. The project has three aims: to compile a database of NMR spectra for large-scale deep learning, to develop an algorithm that incorporates context-dependent considerations, and to apply the algorithm to the discovery of novel small molecules.

The researchers anticipate that the open-source release of this algorithm will significantly benefit scientists in many disciplines and transform drug discovery.

For the full story on all recipients and their projects, view the Office of the Dean for Research homepage story here.