Engineering the Summer is an annual series following UChicago Pritzker Molecular Engineering students as they embark on summer internships and career experiences.
Jianming Mao’s fascination with computational chemistry started in his first year at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME).
Fascinated by the interplay of the micro- and macroscopic world, Mao, now a fifth-year Materials for Sustainability PhD student in the Ferguson Lab, is spending the summer interning at global biopharmaceutical company Alkermes. There, he is helping turn the power and potential of computational chemistry and machine learning into new drugs for a healthier future.
He shared some of his experiences for UChicago PME’s yearly Engineering the Summer series:
What first sparked your interest in your area of study?
My interest in computational chemistry began in the first year of my graduate study, when I first learned to run simulations of biological systems to complement experimental findings. I was fascinated by the idea that we can actually visualize the motions of atoms and molecules, linking the microscopic world of molecular interactions and macroscopic world of biological phenomena.
What research are you focused on at UChicago PME/Chemistry?
At UChicago, my research focuses on leveraging computational chemistry and machine learning for high throughput materials design. I have worked on predicting synthesizability iron-sulfur metal-organic frameworks for catalytic applications, as well as engineering the thermodynamic stability and transport properties of vesicles assembled from polypeptides. Moving forward, I plan to shift toward more data-driven approaches, using machine learning to accelerate the discovery and design of functional materials.
What has been your experience so far this summer at your internship?
This summer, my internship has been an incredibly valuable experience. I’ve had the opportunity to apply the computational and machine learning skills I developed in graduate school to real-world challenges. My work has focused on establishing active learning pipelines that can be readily used by both experimentalists and computational scientists to accelerate the drug design process. One highlight has been collaborating with interdisciplinary teams, which has given me valuable insights into how industry approaches decision-making in early-stage drug discovery.
What do you find most exciting about interning at Alkermes?
What I find most exciting about my internship is the opportunity to contribute to real-world drug discovery efforts. I realized that real-world data is often much messier and more complex than in academic settings. Working through these challenges has been rewarding. It made me think more thoroughly about how I design tools and approaches, and gave me a clearer perspective on the importance of making a method not just accurate, but robust and usable in practice.
What impact do you think your field will have on the world in the next 10 to 20 years?
In the next 10 to 20 years, I believe computational chemistry and machine learning will fundamentally reshape how we design drugs. With the ability to rapidly screen millions of candidates in silico, we can drastically reduce the cost and time required for discovery. Advances in predictive modeling, active learning, and AI-driven design will enable more targeted and efficient solutions to drug discovery and healthcare.
What role do you hope to play in that vision of the future?
In the future, I hope to play a role in advancing research at the intersection of computation and experimental design. I plan to stay actively engaged in research, where I can build computational pipelines and integrate modeling approaches to aid in the design of drugs, materials, and beyond. For me, the ultimate goal of a computational approach is to see it lead to something that works in reality.