About

I fell in love with chemistry in high school - back then it was about making things change color and/or explode. I was sold. My high school was down the street from Yale University and they let a few seniors enroll for a class there each semester. I took freshmen chemistry, which turned out to basically be an applied calculus class in thermodynamics and quantum mechanics. I didn’t know calculus back then.

I shipped up to Boston (as one does) to join the Department of Chemistry and Chemical Biology at Northeastern University. I was totally enchanted by my organic chemistry professor, Prof. Mike Pollastri, and joined his lab to learn synthetic organic chemistry. As I became more familiar with the lab’s focus on medicinal chemistry for neglected tropical disease drug discovery, I became further motivated by the humanitarian nature of the work. I also became more interested in the biological aspects of the work.

From there, I continued with medicinal chemistry in my first co-op (i.e., full-time, 6 month internship between semesters) at GlaxoSmithKline. My co-workers took great interest in the co-ops and gave us the chance to learn about the other aspects of drug discovery programs, from target prioritization to validation to hit to lead. I was particularly interested in the computational team supporting the DNA-encoded library screening platform. I had also fallen in love with computer science in high school, but I didn’t realize I could do both until I saw how they worked.

By chance, my postdoc mentor in Pollastri’s lab, Emanuele Amata, had been doing an externship in the Computational Sciences Center of Excellence in Pfizer possible due to Pollastri’s history of working at Pfizer in Groton (in the good ol’ days). After realizing the potential of computational applications in chemistry and biology, I found my way into doing an internship in the same group where I trained as a software engineer then as a bioinformatician. I followed that experience with many more classes in statistics, mathematics, and computer science before joining the in-silico Lead Discovery team in the Center for Proteomic Chemistry at Novartis where I worked on mechanism of action deconvolution using their “Mechanism of Action Box” screening platform.

I was advised by co-workers at Novartis to move to Bonn, Germany and join the Life Science Informatics master’s program at the Bonn-Aachen International Center for Information Technology in the University of Bonn. So I did. I joined the group of Prof. Martin Hofmann-Apitius jointly with the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) working on using network-based approaches for target identification in neurodegenerative diseases.

That work evolved into my master’s thesis on the development of a general framework for hypothesis generation using knowledge assembly approaches in networks and systems biology. I continued my work during my Ph.D. at Fraunhofer to bridge the gap between systems biology and cheminformatics for mechanistically informed patient stratification, drug identification, and drug repurposing. Following my Ph.D., I had a few months to continue my work in further interfacing machine learning and knowledge graphs in the biomedical domain and applications across a range of topics.

As I entered 2020, I had planned on taking some well-deserved time off. The work I had started was nowhere near done, but it was time for a break before returning to academia for a postdoc. Because the pandemic made it a bad year for a vacation, I spent 10 months at Enveda Biosciences to build their semantics, knowledge graph, and machine learning platforms, which played an important role in securing their second round of venture capital funding. I also joined CoronaWhy to advise on the same topics and reproducibility in computational science.

In 2021, I joined the Laboratory of Systems Pharmacology at Harvard Medical School to continue my academic work. In summer 2023, I moved from Harvard to Northeastern University to continue working under Ben Gyori.

In Spring 2024, I began a sabbatical and will be returning for my next position in Fall 2024.

I’m also a real person, too! I love making music, watching movies/TV, running, and traveling. I’ve been to 30 countries so far and tried beer in almost all of them. I have yet to enjoy it.

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Dr. Charles Tapley Hoyt received his Ph.D. in Computational Life Sciences from the University of Bonn in 2019. His research interests cover the interface of biocuration, knowledge graphs, and machine learning with systems biology, networks biology, and drug discovery. He is an advocate of open source software, reproducibility, and open science. His open source projects such as the Bioregistry, PyBEL, and PyKEEN are used by several academic and industrial groups.