Enoch Toh is a junior pursuing a Bachelor of Science in Molecular and Cellular Biology and Computer Science (CS). He is a recipient of the 2025 Barry Goldwater Scholarship. In an interview with The News-Letter, Toh described his research experience and future plans.
The News-Letter: Could you briefly describe your main research project or research interests?
Enoch Toh: My research is about developing data-driven platforms to guide the design of precision medicines. I'm interested in bridging experimental and computational methods to accelerate the development of genetic cell therapies and to use these platforms to extract insights that can guide the design of next-generation medicines.
N-L: What drew you to this particular field?
ET: I've always been interested in the programmability of biology. In high school, I took a synthetic biology class where we engineered Escherichia coli to remove its pungent odor. That really excited me — how we can engineer and precisely control biological systems. In recent years, cell therapies have become a very promising avenue in medicine, but at the same time, these therapies have become increasingly complex to engineer. So, I think there's this exciting opportunity to leverage computational methods to help improve, accelerate and optimize these systems to be more effective. This convergence of experimental and computational work is where my interests lie.
N-L: When you talk about precision medicines, do you focus on one specific disease or a category?
ET: My main research project is about lipid nanoparticles. Lipid nanoparticles have a lot of applications, from mRNA vaccines, gene editing, cancer immunotherapy and treatment for genetic disorders, but they also require precise optimization. That optimization is what I’m working on: how machine learning and AI-guided systems can develop more efficient and targeted lipid nanoparticles.
N-L: Could you talk about the kind of skills you used or learned?
ET: In the lab, I work mostly on developing high-throughput screening assays, which involves formulating a large library of lipid nanoparticles and testing their performance in cell assays. In terms of computational methods, I worked on developing machine-learning models that could predict the performance of lipid nanoparticles based on their compositions and then used those models to guide optimization. I think of it as a design-build-test kind of work — you do experiments, and you get some data. You use that data to train machine-learning models. The machine-learning model can then tell you what experiment to do next. So it's like a feedback loop, an iterative optimization loop that gradually improves the performance of your nanoparticles.
N-L: How has Hopkins supported your research exploration and independence?
ET: Hopkins has been a really transformative experience for me. I think it has a strong focus on undergraduate research. When I first arrived on campus and I was reaching out for labs to join, I found that there were a lot of resources on how to reach out to professors. There was this platform, ForagerOne, that listed professors and their interests. I emailed a lot [of professors], and my current PI, Dr. Mao, responded. Thanks to that support, I started research in the beginning of my freshman year.
Hopkins also has a lot of funding opportunities to support research endeavors. After freshman year, I applied for the Summer Provost's Undergraduate Research Award that gives you funding to do independent research over the summer. Hopkins also had some undergraduate conference funds that can support your travel to different conferences.
N-L: You're a CS and Molecular and Cellular Biology double major — how do you integrate what you learn in class with your research?
ET: There’s some overlap between class and research. In my CS classes and my biology classes, I’m learning foundational knowledge — cell biology, biochemistry... I think those classes gave me a good foundation to build on. But my lab is more of an engineered, applied lab, so in the lab, I'm taking the knowledge from my classes and trying to apply it to solving problems.
N-L: Were there any challenges that you faced during your research? And how did you overcome them?
ET: I think the research process is a not a linear journey. For me, there were a lot of ups and downs — there were periods where I had very little progress, or very few results. In those periods, it was somewhat challenging and demoralizing. But my PI told me that in these moments, you have to persevere and put in extra effort to stay motivated. He compared the research journey to farming — you spend a long time nurturing and growing your crops, and, finally, you get to harvest all your hard work. So I think that nonlinear trajectory of research was a challenge, but I also think it’s a very rewarding part of doing research as well.
My lab is also very collaborative. People are willing to collaborate with each other all the time and collaborate with other labs on different projects. There was definitely a lot of support there — when I had a problem, I could go to grad students or other undergrads to brainstorm. When working on my project, I also had biweekly meetings with my PI and other students. We would present progress and go over next steps, which was very helpful in keeping me on track.
N-L: What does receiving this scholarship mean to you, both personally and professionally?
ET: Research has been a very big part of my academic journey — and it’s what I want to do in the future. My goals are to go into academia and start my own lab, so receiving this scholarship is definitely very validating for me in terms of recognizing the work that I’ve done over the past few years. But even the process of applying to the Goldwater itself was very rewarding — it challenged me to think about why I’m doing what I’m doing and what my interests really are. It challenged me to do more reflection.
N-L: What future plans do you hold for your research? Are you continuing in this direction or planning to explore new areas?
ET: Currently, I am working on submitting a first-author manuscript, detailing the platform that I’ve developed. That’s my immediate next step. In the future, I’m interested in expanding the scope of my platform — whether it be using more advanced deep-learning models that can give us better mechanistic insights into how lipid nanoparticles are actually working, or whether it be expanding the platform’s optimization — looking at how we can not just optimize carriers, but also the genetic payloads they deliver. I’m interested in continuing to explore how computational tools can accelerate this field.
N-L: What advice do you have for students who want to apply for a scholarship or follow a path like yours in research?
ET: Finding good mentors and a good environment is so important. I was very fortunate to have a supportive and collaborative lab environment. I think that finding an environment where you think that you can do continuous learning is very important — that is, finding a lab that you’re interested in, as well as an environment that supports you to develop those interests and is willing to help find resources to support you.
I think the beauty of academia and academic research is that every path is different. Succeeding in this space is not just about accolades, but finding your own passion and your interests. Academia allows you to develop those interests and turn them into something meaningful.
