This past week I went on an interview and mentor visit, and I began to plan for my original work. First, I had to prepare interview questions for Dr. Son, a professor of experimental chemistry at SMU. I was curious about the difference between computational chemistry and wet lab chemistry, and the interview helped me get a better idea of the work each field entails. At least I know that I have plenty of time to decide, and there is a lot of collaboration between the two types of chemistry. I was surprised to learn that Dr. Son’s research team does little computational or mathematical modeling. Instead, they focus on gathering experimental data and can make precise changes to polymer properties in the lab. When I met with my mentor afterward, I found out that our ADMET prediction paper is under review in a different journal, which hopefully accepts the paper.
Many graph-based prediction models work like a black box, meaning that it’s unclear what atoms or functional groups cause a certain prediction. However, that information is of interest to theoretical chemists to develop new models and to assess whether the results align with our knowledge of chemistry. My original work may relate to this analysis, by analyzing machine learning output based on different versions of a molecule, and I'm excited to write code for this. This week, unfortunately, my interview got rescheduled, but I’m looking forward to Business Symposium and to learning more through my research assessment.
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