Sereina Riniker

Prof. Sereina Riniker was born and raised in Switzerland. In 2008, she completed her Master’s degree in chemistry at ETH Zurich. After an internship in the research department of Givaudan AG and a research stay at the University of California Berkeley, she returned in 2009 to ETH Zurich to obtain a PhD in molecular dynamics simulations. From 2012 to 2014, she held a postdoctoral position in cheminformatics at the Novartis Institutes for BioMedical Research in Basel and Cambridge, Massachusetts. In June 2014, she became Assistant Professor at ETH Zurich, and was shortly thereafter featured as one of the most promising young scientists in the world in Forbes magazine’s “30 under 30” issue. She is currently Associate Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences at ETH Zurich. In 2020, Riniker won a MARVEL Agility Plus grant for work on improving the generation and sampling of crystal packing with machine learning.

Interview by Carey Sargent, EPFL, NCCR MARVEL, Mai 2020

The biggest challenge women scientists face is...

The most important is to look forward, to do our share and be role models for the next generation.

I chose a scientific career because…

I want to know how things work. I find it super fascinating how biological processes work in atomistic detail, how everything is connected. I like to understand mechanisms, not only in nature but also generally in engineering, society, etc.

If I weren’t a scientist, I would be…

I think I would have done something connected with arts. There are many similarities between arts and science, both are driven by curiosity and joy to experiment.

What is your greatest MARVEL discovery to date?

We have just joined MARVEL. I’m looking forward to exciting collaborations within this project.

My top two papers...

I like the papers where we presented a new idea or a proof of concept and thus these studies turned out to be stepping stones for whole research directions. In this respect, our paper on "replica-exchange enveloping distribution to estimate free-energy differences" for multiple states in a single simulation, and the one showcasing the idea of "molecular dynamics fingerprints as descriptors for machine learning" are some of my favorites.